- Survey Paper
- Open access
- Published: 09 December 2019
Internet of Things is a revolutionary approach for future technology enhancement: a review
- Sachin Kumar ORCID: orcid.org/0000-0003-3949-0302 1 ,
- Prayag Tiwari 2 &
- Mikhail Zymbler 1
Journal of Big Data volume 6 , Article number: 111 ( 2019 ) Cite this article
Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.
The Internet of Things (IoT) is an emerging paradigm that enables the communication between electronic devices and sensors through the internet in order to facilitate our lives. IoT use smart devices and internet to provide innovative solutions to various challenges and issues related to various business, governmental and public/private industries across the world [ 1 ]. IoT is progressively becoming an important aspect of our life that can be sensed everywhere around us. In whole, IoT is an innovation that puts together extensive variety of smart systems, frameworks and intelligent devices and sensors (Fig. 1 ). Moreover, it takes advantage of quantum and nanotechnology in terms of storage, sensing and processing speed which were not conceivable beforehand [ 2 ]. Extensive research studies have been done and available in terms of scientific articles, press reports both on internet and in the form of printed materials to illustrate the potential effectiveness and applicability of IoT transformations. It could be utilized as a preparatory work before making novel innovative business plans while considering the security, assurance and interoperability.
General architecture of IoT
A great transformation can be observed in our daily routine life along with the increasing involvement of IoT devices and technology. One such development of IoT is the concept of Smart Home Systems (SHS) and appliances that consist of internet based devices, automation system for homes and reliable energy management system [ 3 ]. Besides, another important achievement of IoT is Smart Health Sensing system (SHSS). SHSS incorporates small intelligent equipment and devices to support the health of the human being. These devices can be used both indoors and outdoors to check and monitor the different health issues and fitness level or the amount of calories burned in the fitness center etc. Also, it is being used to monitor the critical health conditions in the hospitals and trauma centers as well. Hence, it has changed the entire scenario of the medical domain by facilitating it with high technology and smart devices [ 4 , 5 ]. Moreover, IoT developers and researchers are actively involved to uplift the life style of the disabled and senior age group people. IoT has shown a drastic performance in this area and has provided a new direction for the normal life of such people. As these devices and equipment are very cost effective in terms of development cost and easily available within a normal price range, hence most of the people are availing them [ 6 ]. Thanks to IoT, as they can live a normal life. Another important aspect of our life is transportation. IoT has brought up some new advancements to make it more efficient, comfortable and reliable. Intelligent sensors, drone devices are now controlling the traffic at different signalized intersections across major cities. In addition, vehicles are being launched in markets with pre-installed sensing devices that are able to sense the upcoming heavy traffic congestions on the map and may suggest you another route with low traffic congestion [ 7 ]. Therefore IoT has a lot to serve in various aspects of life and technology. We may conclude that IoT has a lot of scope both in terms of technology enhancement and facilitate the humankind.
IoT has also shown its importance and potential in the economic and industrial growth of a developing region. Also, in trade and stock exchange market, it is being considered as a revolutionary step. However, security of data and information is an important concern and highly desirable, which is a major challenging issue to deal with [ 5 ]. Internet being a largest source of security threats and cyber-attacks has opened the various doors for hackers and thus made the data and information insecure. However, IoT is committed to provide the best possible solutions to deal with security issues of data and information. Hence, the most important concern of IoT in trade and economy is security. Therefore, the development of a secure path for collaboration between social networks and privacy concerns is a hot topic in IoT and IoT developers are working hard for this.
The remaining part of the article is organized as follows: “ Literature survey ” section will provide state of art on important studies that addressed various challenges and issues in IoT. “ IoT architecture and technologies ” section discussed the IoT functional blocks, architecture in detail. In “ Major key issues and challenges of IoT ” section, important key issues and challenges of IoT is discussed. “ Major IoT applications ” section provides emerging application domains of IoT. In “ Importance of big data analytics in IoT ” section, the role and importance of big data and its analysis is discussed. Finally, the article concluded in “ Conclusions ” section.
IoT has a multidisciplinary vision to provide its benefit to several domains such as environmental, industrial, public/private, medical, transportation etc. Different researchers have explained the IoT differently with respect to specific interests and aspects. The potential and power of IoT can be seen in several application domains. Figure 2 illustrates few of the application domains of IoTs potentials.
Some of the potential application domains of IoT
Various important IoT projects have taken charge over the market in last few years. Some of the important IoT projects that have captured most of the market are shown in Fig. 3 . In Fig. 3 , a global distribution of these IoT projects is shown among American, European and Asia/Pacific region. It can be seen that American continent are contributing more in the health care and smart supply chain projects whereas contribution of European continent is more in the smart city projects [ 8 ].
Global distribution of IoT projects among America (USA, South America and Canada), Europe and APAC (Asia and Pacific region) [ 8 ]
Figure 4 , illustrates the global market share of IoT projects worldwide [ 8 ]. It is evident that industry, smart city, smart energy and smart vehicle based IoT projects have a big market share in comparison to others.
Global share of IoT projects across the world [ 8 ]
Smart city is one of the trendy application areas of IoT that incorporates smart homes as well. Smart home consists of IoT enabled home appliances, air-conditioning/heating system, television, audio/video streaming devices, and security systems which are communicating with each other in order to provide best comfort, security and reduced energy consumption. All this communication takes place through IoT based central control unit using Internet. The concept of smart city gained popularity in the last decade and attracted a lot of research activities [ 9 ]. The smart home business economy is about to cross the 100 billion dollars by 2022 [ 10 ]. Smart home does not only provide the in-house comfort but also benefits the house owner in cost cutting in several aspects i.e. low energy consumption will results in comparatively lower electricity bill. Besides smart homes, another category that comes within smart city is smart vehicles. Modern cars are equipped with intelligent devices and sensors that control most of the components from the headlights of the car to the engine [ 11 ]. The IoT is committed towards developing a new smart car systems that incorporates wireless communication between car-to-car and car-to-driver to ensure predictive maintenance with comfortable and safe driving experience [ 12 ].
Khajenasiri et al. [ 10 ] performed a survey on the IoT solutions for smart energy control to benefit the smart city applications. They stated that at present IoT has been deployed in very few application areas to serve the technology and people. The scope of IoT is very wide and in near future IoT is able to capture almost all application areas. They mentioned that energy saving is one of the important part of the society and IoT can assist in developing a smart energy control system that will save both energy and money. They described an IoT architecture with respect to smart city concept. The authors also discussed that one of the challenging task in achieving this is the immaturity of IoT hardware and software. They suggested that these issues must be resolved to ensure a reliable, efficient and user friendly IoT system.
Alavi et al. [ 13 ] addressed the urbanization issue in the cities. The movement of people from rural to urban atmosphere resulting in growing population of the cities. Therefore, there is a need to provide smart solutions for mobility, energy, healthcare and infrastructure. Smart city is one of the important application areas for IoT developers. It explores several issues such as traffic management, air quality management, public safety solutions, smart parking, smart lightning and smart waste collection (Fig. 5 ). They mentioned that IoT is working hard to tackle these challenging issues. The need for improved smart city infrastructure with growing urbanization has opened the doors for entrepreneurs in the field of smart city technologies. The authors concluded that IoT enabled technology is very important for the development of sustainable smart cities.
Potential IoT application areas for smart cities
Another important issue of IoT that requires attention and a lot of research is security and privacy. Weber [ 14 ] focused on these issues and suggested that a private organization availing IoT must incorporate data authentication, access control, resilience to attacks and client privacy into their business activities that would be an additional advantage. Weber suggested that in order to define global security and privacy issues, IoT developers must take into account the geographical limitations of the different countries. A generic framework needs to be designed to fit the global needs in terms of privacy and security. It is highly recommended to investigate and recognize the issues and challenges in privacy and security before developing the full fledge working IoT framework.
Later, Heer et al. [ 15 ] came up with a security issue in IP based IoT system. They mentioned that internet is backbone for the communication among devices that takes place in an IoT system. Therefore, security issues in IP based IoT systems are an important concern. In addition, security architecture should be designed considering the life cycle and capabilities of any object in the IoT system. It also includes the involvement of the trusted third party and the security protocols. The security architecture with scalability potential to serve the small-scale to large-scale things in IoT is highly desirable. The study pointed out that IoT gave rise to a new way of communication among several things across the network therefore traditional end to end internet protocol are not able to provide required support to this communication. Therefore, new protocols must be designed considering the translations at the gateways to ensure end-to-end security. Moreover, all the layers responsible for communication has their own security issues and requirements. Therefore, satisfying the requirements for one particular layers will leave the system into a vulnerable state and security should be ensured for all the layers.
Authentication and access control is another issue in IoT that needs promising solutions to strengthen the security. Liu et al. [ 16 ] brought up a solution to handle authentication and access control. Authentication is very important to verify the communicating parties to prevent the loss of confidential information. Liu et al. [ 16 ] provided an authentication scheme based on Elliptic Curve Cryptosystem and verified it on different security threats i.e. eavesdropping, man-in-the-middle attack, key control and replay attack. They claimed that there proposed schemes are able to provide better authentication and access control in IoT based communication. Later, Kothmayr et al. [ 17 ] proposed a two-way authentication scheme based of datagram transport layer security (DTLS) for IoT. The attackers over the internet are always active to steal the secured information. The proposed approach are able to provide message security, integrity, authenticity and confidentiality, memory overhead and end-to-end latency in the IoT based communication network.
Li et al. [ 18 ] proposed a dynamic approach for data centric IoT applications with respect to cloud platforms. The need of an appropriate device, software configuration and infrastructure requires efficient solutions to support massive amount of IoT applications that are running on cloud platforms. IoT developers and researchers are actively engaged in developing solutions considering both massive platforms and heterogeneous nature of IoT objects and devices. Olivier et al. [ 19 ] explained the concept of software defined networking (SDN) based architecture that performs well even if a well-defined architecture is not available. They proposed that SDN based security architecture is more flexible and efficient for IoT.
Luk et al. [ 20 ] stated that the main task of a secure sensor network (SSN) is to provide data privacy, protection from replay attacks and authentication. They discussed two popular SSN services namely TinySec [ 21 ] and ZigBee [ 22 ]. They mentioned that although both the SSN services are efficient and reliable, however, ZigBee is comparatively provides higher security but consumes high energy whereas TinySec consumes low energy but not as highly secured as ZigBee. They proposed another architecture MiniSec to support high security and low energy consumption and demonstrated its performance for the Telos platform. Yan et al. [ 23 ] stated that trust management is an important issue in IoT. Trust management helps people to understand and trust IoT services and applications without worrying about uncertainty issues and risks [ 24 ]. They investigated different issues in trust management and discussed its importance with respect to IoT developers and users.
Noura et al. [ 25 ] stated the importance of interoperability in IoT as it allows integration of devices, services from different heterogeneous platforms to provide the efficient and reliable service. Several other studies focused on the importance of interoperability and discussed several challenges that interoperability issue is facing in IoT [ 26 , 27 , 28 ]. Kim et al. [ 29 ] addressed the issue of climate change and proposed an IoT based ecological monitoring system. They mentioned that existing approaches are time consuming and required a lot of human intervention. Also, a routine visit is required to collect the information from the sensors installed at the site under investigation. Also, some information remained missing which leads to not highly accurate analysis. Therefore, IoT based framework is able to solve this problem and can provide high accuracy in analysis and prediction. Later, Wang et al. [ 30 ] shows their concern for domestic waste water treatment. They discussed several deficiencies in the process of waste water treatment and dynamic monitoring system and suggested effective solutions based on IoT. They stated that IoT can be very effective in the waste water treatment and process monitoring.
Agriculture is one of the important domain around the world. Agriculture depends on several factors i.e. geographical, ecological etc. Qiu et al. [ 31 ] stated that technology that is being used for ecosystem control is immature with low intelligence level. They mentioned that it could be a good application area for IoT developers and researchers.
Qiu et al. [ 31 ] proposed an intelligent monitoring platform framework for facility agriculture ecosystem based on IoT that consists of four layer mechanism to manage the agriculture ecosystem. Each layer is responsible for specific task and together the framework is able to achieve a better ecosystem with reduced human intervention.
Another important concern around the world is climate change due to global warming. Fang et al. [ 32 ] introduced an integrated information system (IIS) that integrates IoT, geo-informatics, cloud computing, global positioning system (GPS), geographical information system (GIS) and e-science in order to provide an effective environmental monitoring and control system. They mentioned that the proposed IIS provides improved data collection, analysis and decision making for climate control. Air pollution is another important concern worldwide. Various tools and techniques are available to air quality measures and control. Cheng et al. [ 33 ] proposed AirCloud which is a cloud based air quality and monitoring system. They deployed AirCloud and evaluated its performance using 5 months data for the continuous duration of 2 months.
Temglit et al. [ 34 ] considered Quality of Service (QoS) as an important challenge and a complex task in evaluation and selection of IoT devices, protocols and services. QoS is very important criteria to attract and gain trust of users towards IoT services and devices. They came up with an interesting distributed QoS selection approach. This approach was based on distributed constraint optimization problem and multi-agent paradigm. Further, the approach was evaluated based on several experiments under realistic distributed environments. Another important aspect of IoT is its applicability to the environmental and agriculture standards. Talavera et al. [ 35 ] focused in this direction and presented the fundamental efforts of IoT for agro-industrial and environmental aspects in a survey study. They mentioned that the efforts of IoT in these areas are noticeable. IoT is strengthening the current technology and benefiting the farmers and society. Jara et al. [ 36 ] discussed the importance of IoT based monitoring of patients health. They suggested that IoT devices and sensors with the help of internet can assist health monitoring of patients. They also proposed a framework and protocol to achieve their objective. Table 1 provides a summary of the important studies and the direction of research with a comparison of studies on certain evaluation parameters.
IoT architecture and technologies
The IoT architecture consists of five important layers that defines all the functionalities of IoT systems. These layers are perception layer, network layer, middleware layer, application layer, business layer. At the bottom of IoT architecture, perception layer exists that consists of physical devices i.e. sensors, RFID chips, barcodes etc. and other physical objects connected in IoT network. These devices collects information in order to deliver it to the network layer. Network layer works as a transmission medium to deliver the information from perception layer to the information processing system. This transmission of information may use any wired/wireless medium along with 3G/4G, Wi-Fi, Bluetooth etc. Next level layer is known as middleware layer. The main task of this layer is to process the information received from the network layer and make decisions based on the results achieved from ubiquitous computing. Next, this processed information is used by application layer for global device management. On the top of the architecture, there is a business layer which control the overall IoT system, its applications and services. The business layer visualizes the information and statistics received from the application layer and further used this knowledge to plan future targets and strategies. Furthermore, the IoT architectures can be modified according to the need and application domain [ 19 , 20 , 37 ]. Besides layered framework, IoT system consists of several functional blocks that supports various IoT activities such as sensing mechanism, authentication and identification, control and management [ 38 ]. Figure 6 illustrates such functional blocks of IoT architecture.
A generic function module of IoT system
There are several important functional blocks responsible for I/O operations, connectivity issues, processing, audio/video monitoring and storage management. All these functional block together incorporates an efficient IoT system which are important for optimum performance. Although, there are several reference architectures proposed with the technical specifications, but these are still far from the standard architecture that is suitable for global IoT [ 39 ]. Therefore, a suitable architecture is still needsvk to be designed that could satisfy the global IoT needs. The generic working structure of IoT system is shown in Fig. 7 . Figure 7 shows a dependency of IoT on particular application parameters. IoT gateways have an important role in IoT communication as it allows connectivity between IoT servers and IoT devices related to several applications [ 40 ].
Working structure of IoT
Scalability, modularity, interoperability and openness are the key design issues for an efficient IoT architecture in a heterogenous environment. The IoT architecture must be designed with an objective to fulfil the requirements of cross domain interactions, multi-system integration with the potential of simple and scalable management functionalities, big data analytics and storage, and user friendly applications. Also, the architecture should be able to scaleup the functionality and add some intelligence and automation among the IoT devices in the system.
Moreover, increasing amount of massive data being generated through the communication between IoT sensors and devices is a new challenge. Therefore, an efficient architecture is required to deal with massive amount of streaming data in IoT system. Two popular IoT system architectures are cloud and fog/edge computing that supports with the handling, monitoring and analysis of huge amount of data in IoT systems. Therefore, a modern IoT architecture can be defined as a 4 stage architecture as shown in Fig. 8 .
Four stage IoT architecture to deal with massive data
In stage 1 of the architecture, sensors and actuators plays an important role. Real world is comprised of environment, humans, animals, electronic gadgets, smart vehicles, and buildings etc. Sensors detect the signals and data flow from these real world entities and transforms into data which could further be used for analysis. Moreover, actuators is able to intervene the reality i.e. to control the temperature of the room, to slow down the vehicle speed, to turn off the music and light etc. Therefore, stage 1 assist in collecting data from real world which could be useful for further analysis. Stage 2 is responsible to collaborate with sensors and actuators along with gateways and data acquisition systems. In this stage, massive amount of data generated in stage 1 is aggregated and optimized in a structured way suitable for processing. Once the massive amount of data is aggregated and structured then it is ready to be passed to stage 3 which is edge computing. Edge computing can be defined as an open architecture in distributed fashion which allows use of IoT technologies and massive computing power from different locations worldwide. It is very powerful approach for streaming data processing and thus suitable for IoT systems. In stage 3, edge computing technologies deals with massive amount of data and provides various functionalities such as visualization, integration of data from other sources, analysis using machine learning methods etc. The last stage comprises of several important activities such as in depth processing and analysis, sending feedback to improve the precision and accuracy of the entire system. Everything at this stage will be performed on cloud server or data centre. Big data framework such as Hadoop and Spark may be utilized to handle this large streaming data and machine learning approaches can be used to develop better prediction models which could help in a more accurate and reliable IoT system to meet the demand of present time.
Major key issues and challenges of IoT
The involvement of IoT based systems in all aspects of human lives and various technologies involved in data transfer between embedded devices made it complex and gave rise to several issues and challenges. These issues are also a challenge for the IoT developers in the advanced smart tech society. As technology is growing, challenges and need for advanced IoT system is also growing. Therefore, IoT developers need to think of new issues arising and should provide solutions for them.
Security and privacy issues
One of the most important and challenging issues in the IoT is the security and privacy due to several threats, cyber attacks, risks and vulnerabilities [ 41 ]. The issues that give rise to device level privacy are insufficient authorization and authentication, insecure software, firmware, web interface and poor transport layer encryption [ 42 ]. Security and privacy issues are very important parameters to develop confidence in IoT Systems with respect to various aspects [ 43 ]. Security mechanisms must be embedded at every layer of IoT architecture to prevent security threats and attacks [ 23 ]. Several protocols are developed and efficiently deployed on every layer of communication channel to ensure the security and privacy in IoT based systems [ 44 , 45 ]. Secure Socket Layer (SSL) and Datagram Transport Layer Security (DTLS) are one of the cryptographic protocols that are implemented between transport and application layer to provide security solutions in various IoT systems [ 44 ]. However, some IoT applications require different methods to ensure the security in communication between IoT devices. Besides this, if communication takes place using wireless technologies within the IoT system, it becomes more vulnerable to security risks. Therefore, certain methods should be deployed to detect malicious actions and for self healing or recovery. Privacy on the other hand is another important concern which allows users to feel secure and comfortable while using IoT solutions. Therefore, it is required to maintain the authorization and authentication over a secure network to establish the communication between trusted parties [ 46 ]. Another issue is the different privacy policies for different objects communicating within the IoT system. Therefore, each object should be able to verify the privacy policies of other objects in IoT system before transmitting the data.
Interoperability is the feasibility to exchange the information among different IoT devices and systems. This exchange of information does not rely on the deployed software and hardware. The interoperability issue arises due to the heterogeneous nature of different technology and solutions used for IoT development. The four interoperability levels are technical, semantic, syntactic and organizational [ 47 ]. Various functionalities are being provided by IoT systems to improve the interoperability that ensures communication between different objects in a heterogeneous environment. Additionally, it is possible to merge different IoT platforms based on their functionalities to provide various solutions for IoT users [ 48 ]. Considering interoperability an important issue, researchers approved several solutions that are also know as interoperability handling approaches [ 49 ]. These solutions could be adapaters/gateways based, virtual networks/overlay based, service oriented architecture based etc. Although interoperability handling approaches ease some pressure on IoT systems but there are still certain challenges remain with interoperability that could be a scope for future studies [ 25 ].
Ethics, law and regulatory rights
Another issue for IoT developers is the ethics, law and regulatory rights. There are certain rules and regulations to maintain the standard, moral values and to prevent the people from violating them. Ethics and law are very similar term with the only difference is that ethics are standards that people believes and laws are certain restrictions decided by the government. However, both ethics and laws are designed to maintain the standard, quality and prevent people from illegal use. With the development of IoT, several real life problems are solved but it has also given rise to critical ethical and legal challenges [ 50 ]. Data security, privacy protection, trust and safety, data usability are some of those challenges. It has also been observed that majority of IoT users are supporting government norms and regulations with respect to data protection, privacy and safety due to the lack of trust in IoT devices. Therefore, this issue must be taken into consideration to maintain and improve the trust among people for the use of IoT devices and systems.
Scalability, availability and reliability
A system is scalable if it is possible to add new services, equipments and devices without degrading its performance. The main issue with IoT is to support a large number of devices with different memory, processing, storage power and bandwidth [ 28 ]. Another important issue that must be taken into consideration is the availability. Scalability and availability both should be deployed together in the layered framework of IoT. A great example of scalability is cloud based IoT systems which provide sufficient support to scale the IoT network by adding up new devices, storage and processing power as required.
However, this global distributed IoT network gives rise to a new research paradigm to develop a smooth IoT framework that satisfy global needs [ 51 ]. Another key challenge is the availability of resources to the authentic objects regardless of their location and time of the requirement. In a distributed fashion, several small IoT networks are timely attached to the global IoT platforms to utilize their resources and services. Therefore, availability is an important concern [ 52 ]. Due to the use of different data transmission channels i.e. satellite communication, some services and availability of resources may be interrupted. Therefore, an independent and reliable data transmission channel is required for uninterrupted availability of resources and services.
Quality of Service (QoS)
Quality of Service (QoS) is another important factor for IoT. QoS can be defined as a measure to evaluate the quality, efficiency and performance of IoT devices, systems and architecture [ 34 ]. The important and required QoS metrics for IoT applications are reliability, cost, energy consumption, security, availability and service time [ 53 ]. A smarter IoT ecosystem must fulfill the requirements of QoS standards. Also, to ensure the reliability of any IoT service and device, its QoS metrics must be defined first. Further, users may also be able to specifiy their needs and requirements accordingly. Several approaches can be deployed for QoS assessment, however as mentioned by White et al. [ 54 ] there is a trade-off between quality factors and approaches. Therefore, good quality models must be deployed to overcome this trade-off. There are certain good quality models available in literature such as ISO/IEC25010 [ 55 ] and OASIS-WSQM [ 56 ] which can be used to evaluate the approaches used for QoS assessment. These models provides a wide range of quality factors that is quite sufficient for QoS assessment for IoT services. Table 2 summarizes the different studies with respect to IoT key challenges and issues discussed above.
Major IoT applications
Emerging economy, environmental and health-care.
IoT is completely devoted to provide emerging public and financial benefits and development to the society and people. This includes a wide range of public facilities i.e. economic development, water quality maintenance, well-being, industrialization etc. Overall, IoT is working hard to accomplish the social, health and economic goals of United Nations advancement step. Environmental sustainability is another important concern. IoT developers must be concerned about environmental impact of the IoT systems and devices to overcome the negative impact [ 48 ]. Energy consumption by IoT devices is one of the challenges related to environmental impact. Energy consumption is increasing at a high rate due to internet enabled services and edge cutting devices. This area needs research for the development of high quality materials in order to create new IoT devices with lower energy consumption rate. Also, green technologies can be adopted to create efficient energy efficient devices for future use. It is not only environmental friendly but also advantageous for human health. Researchers and engineers are engaged in developing highly efficient IoT devices to monitor several health issues such as diabetes, obesity or depression [ 57 ]. Several issues related to environment, energy and healthcare are considered by several studies.
Smart city, transport and vehicles
IoT is transforming the traditional civil structure of the society into high tech structure with the concept of smart city, smart home and smart vehicles and transport. Rapid improvements are being done with the help of supporting technologies such as machine learning, natural language processing to understand the need and use of technology at home [ 58 ]. Various technologies such as cloud server technology, wireless sensor networks that must be used with IoT servers to provide an efficient smart city. Another important issue is to think about environmental aspect of smart city. Therefore, energy efficient technologies and Green technologies should also be considered for the design and planning of smart city infrastructure. Further, smart devices which are being incorporated into newly launched vehicles are able to detect traffic congestions on the road and thus can suggest an optimum alternate route to the driver. This can help to lower down the congestion in the city. Furthermore, smart devices with optimum cost should be designed to be incorporated in all range vehicles to monitor the activity of engine. IoT is also very effective in maintaining the vehicle’s health. Self driving cars have the potential to communicate with other self driving vehicles by the means of intelligent sensors. This would make the traffic flow smoother than human-driven cars who used to drive in a stop and go manner. This procedure will take time to be implemented all over the world. Till the time, IoT devices can help by sensing traffic congestion ahead and can take appropriate actions. Therefore, a transport manufacturing company should incorporate IoT devices into their manufactured vehicles to provide its advantage to the society.
Agriculture and industry automation
The world’s growing population is estimated to reach approximate 10 billion by 2050. Agriculture plays an important role in our lives. In order to feed such a massive population, we need to advance the current agriculture approaches. Therefore, there is a need to combine agriculture with technology so that the production can be improved in an efficient way. Greenhouse technology is one of the possible approaches in this direction. It provides a way to control the environmental parameters in order to improve the production. However, manual control of this technology is less effective, need manual efforts and cost, and results in energy loss and less production. With the advancement of IoT, smart devices and sensors makes it easier to control the climate inside the chamber and monitor the process which results in energy saving and improved production (Fig. 9 ). Automatization of industries is another advantage of IoT. IoT has been providing game changing solutions for factory digitalization, inventory management, quality control, logistics and supply chain optimization and management.
A working structure of IoT system in agriculture production
Importance of big data analytics in IoT
An IoT system comprises of a huge number of devices and sensors that communicates with each other. With the extensive growth and expansion of IoT network, the number of these sensors and devices are increasing rapidly. These devices communicate with each other and transfer a massive amount of data over internet. This data is very huge and streaming every second and thus qualified to be called as big data. Continuous expansion of IoT based networks gives rise to complex issue such as management and collection of data, storage and processing and analytics. IoT big data framework for smart buildings is very useful to deal with several issues of smart buildings such as managing oxygen level, to measure the smoke/hazardous gases and luminosity [ 59 ]. Such framework is capable to collect the data from the sensors installed in the buildings and performs data analytics for decision making. Moreover, industrial production can be improved using an IoT based cyber physical system that is equipped with an information analysis and knowledge acquisition techniques [ 60 ]. Traffic congestion is an important issue with smart cities. The real time traffic information can be collected through IoT devices and sensors installed in traffic signals and this information can be analyzed in an IoT based traffic management system [ 61 ]. In healthcare analysis, the IoT sensors used with patients generate a lot of information about the health condition of patients every second. This large amount of information needs to be integrated at one database and must be processed in real time to take quick decision with high accuracy and big data technology is the best solution for this job [ 62 ]. IoT along with big data analytics can also help to transform the traditional approaches used in manufacturing industries into the modern one [ 63 ]. The sensing devices generates information which can be analyzed using big data approaches and may help in various decision making tasks. Furthermore, use of cloud computing and analytics can benefit the energy development and conservation with reduced cost and customer satisfaction [ 64 ]. IoT devices generate a huge amount of streaming data which needs to be stored effectively and needs further analysis for decision making in real time. Deep learning is very effective to deal with such a large information and can provide results with high accuracy [ 65 ]. Therefore, IoT, Big data analytics and Deep learning together is very important to develop a high tech society.
Recent advancements in IoT have drawn attention of researchers and developers worldwide. IoT developers and researchers are working together to extend the technology on large scale and to benefit the society to the highest possible level. However, improvements are possible only if we consider the various issues and shortcomings in the present technical approaches. In this survey article, we presented several issues and challenges that IoT developer must take into account to develop an improved model. Also, important application areas of IoT is also discussed where IoT developers and researchers are engaged. As IoT is not only providing services but also generates a huge amount of data. Hence, the importance of big data analytics is also discussed which can provide accurate decisions that could be utilized to develop an improved IoT system.
Availability of data and materials
Internet of Things
Quality of Service
Web of Things
Cloud of Things
Smart Home System
Smart Health Sensing System
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This work was financially supported by the Ministry of Education and Science of Russian Federation (government order 2.7905.2017/8.9).
The research received no external funding.
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Sachin Kumar & Mikhail Zymbler
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Kumar, S., Tiwari, P. & Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: a review. J Big Data 6 , 111 (2019). https://doi.org/10.1186/s40537-019-0268-2
Received : 24 July 2019
Accepted : 10 November 2019
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DOI : https://doi.org/10.1186/s40537-019-0268-2
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- Internet of Things (IoT)
- IoT architecture
- IoT challenges
- IoT applications
the world is experiencing a strong rush towards modern technology, while specialized companies are living a terrible rush in the information technology towards the so-called Internet of things IoT or Internet of objects, which is the integration of things with the world of Internet, by adding hardware or/and software to be smart and so be able to communicate with each other and participate effectively in all aspects of daily life, so enabling new forms of communication between people and things, and between things themselves, that’s will change the traditional life into a high style of living. But it won’t be easy, because there are still many challenges an d issues that need to be addressed and have to be viewed from various aspects to realize their full potential. The main objective of this review paper will provide the reader with a detailed discussion from a technological and social perspective. The various IoT challenges and issues, definition and architecture were discussed. Furthermore, a description of several sensors and actuators and their smart communication. Also, the most important application areas of IoT were presented. This work will help readers and researchers understand the IoT and its potential application in the real world.
Internet of things (IoT) , Smart Communication , Sensors , Actuators , System integration , Smart house/city , Network interface
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The term “Internet of Things” (IoT) was coined by Kevin Ashton at a presentation to Proctor & Gamble in 1999. He is one of the founders of the Massachusetts Institute of Technology’s Automatic Recognition Lab. He pioneered RFID (used in barcode detector) technology in the field of supply chain management. He also founded Zensi, a company that manufactures energy sensing and monitoring technologies.
The Internet of Things is an emerging topic of technical, social and economic importance. Consumer products, durable goods, cars and trucks, industrial components and facilities, sensors, and other everyday objects are combined with internet connectivity and powerful data analysis capabilities that promise to transform the way we live and work.
A major shift in our daily routines can be observed along with the widespread implementation of IoT devices and technologies. IoT is everywhere, although we don’t always see it or know that a device is part of it. For consumers, new IoT products like Internet-enabled devices, home automation components and power management devices drives us toward seeing “Smart home”, which provides more safety and energy efficiency. Other IoT personal devices such as wearable fitness and health monitors that support the network-enabled medical devices are transforming the way healthcare services are delivered. The Internet of Things transforms physical objects into an information ecosystem shared between wearable, portable, and even implantable devices, making our life technology and data rich.
The IoT technology promises to be useful for people with disabilities and the elderly, allowing for improved levels of independence quality of life at reasonable cost . Internet of things systems such as networked vehicles, smart traffic systems, and sensors embedded in roads and bridges bring us closer to the idea of “smart cities”, which help reduce congestion and energy consumption. IoT technology offers the potential to transform agriculture, industry, and energy production and distribution is increasing availability of information along the production value chain using networked sensors.
A number of companies and research organizations have provided a wide range of expectations about the potential impact of the Internet of Things on Internet and the economy over the next decade. Huawei expects 100 billion IoT connections by 2025 . Manyika et al.  estimating the potential economic impact of the Internet of Things from $3.9 to $11 trillion annually in 2025, driven by: Lower device prices, advanced cloud storage computing, higher speed and lower delivery costs. This increases the number of machines and devices connected to the Internet. Also estimated (2015) that the Internet of Things will contribute 4% - 11% of global GDP in 2025.
However, at the same time, the Internet of Things raises significant challenges that could stand in the way of realizing its potential benefits. Attention-grabbing headlines about internet device hacking , surveillance concerns , and privacy concerns have already captured the public’s attention Technical challenges remain, and new political, legal and development challenges arise. This discussion is “promise versus risk” along with the flow of information through popular media and Marketing can make the Internet of Things a complex topic to understand.
This overview paper is designed to help readers and researchers understanding the IoT potential benefits and most key issues that face it. The paper is organized as follows: Section 2 provides a definition and literature review of IoT; Section 3: describes the components of IoT architecture; Section 4: Sensors and actuators are discussed followed by section 5: identifies important key issues and challenges then communication stage in Section 6. Finally, section 7 provides emerging application domains of IoT.
2. Definition and Literature Review
The definition of the Internet of Things (IoT) is not definitively limited and not currently defined, meaning that there is no general definition approved by the majority or by the global user community, and therefore the Internet of Things is maturing and continuing to be the newest, most popular concept in the world of information technology.
The “Thing” in IoT can be any device with any type of sensor embedded with the ability to collect data and transmit it across the network without manual intervention. The technology embedded in the object helps to interact with internal states and the external environment, which in turn aids in the decision-making process.
The Internet of Things (IoT) is a framework in which all things have a representation and a presence in the Internet. More specifically, the Internet of Things aims at offering new applications and services bridging the physical and virtual worlds, in which Machine-to-Machine (M2M) communications represents the baseline communication that enables the interactions between Things and applications in the cloud. This is defined by IEEE communication magazine .
Oxford Dictionaries provides a summary definition that calls the Internet as an element of IoT: “Internet of things (noun): The interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data” .
The Internet of Things creates an inclusive information system, which consists of smaller information systems; Smart devices are connected to the smart home system and connected to smart city systems. In reality, the Internet of Things is far more complicated than that.
2.2. Literature Review
The field of Internet of Things leads to a world of technology and to a new era where things can communicate, calculate and transform information fastly. This technology has attracted many researchers who have provided their own researches.
The main problem with the Internet of Things is that it is very broad and unlimited, so to implement its concept is fundamentally dependent on its architecture. In the initial stage of research, the three layer architecture was introduced .
3.1. The Three Layer Architecture
This architecture consists of three layers. First is the perception layer which is the physical layer. It has sensors for sensing and gathering information about the environment. It senses some physical parameters or identifies other smart objects in the environment. The second is the network layer which is responsible for connecting to other smart things, network devices, and servers. It is also characterized by transmitting and processing sensor data. The third one is the application layer which is responsible for delivering application specific services to the user. It defines various applications in which the Internet of things can be deployed as title of example: smart houses, smart cities and smart health. Figure 1 depicted this architecture.
Figure 1 . 3 Layer architecture
Figure 2 . 5 Layer architecture.
3.2. The Five Layer Architecture
The Five-layer architecture  is designed to define the complete concept of its functioning and development of IoT devices. The new structure includes 5 layers as shown in Figure 2 . Layers of perception and application function in the same way as in the previous architecture. The main task of processing layer is to process the information received from the network layer and to make decisions based on the results achieved from ubiquitous computing. The transport layer that transfers sensor data from the perception layer to the processing layer and vice versa over networks such as wireless, LAN, 3G, LTE, RFID and Bluetooth. Finally, the business layer visualizes information and statistics from the application layer and uses this knowledge to plan future goals and strategies
4. Sensors & Actuators
Sensors and actuators are among the building blocks of IoT. In many IoT applications, you need one or more sensors to collect data and information about the system. The data is processed, and commands may be issued to the triggers that in turn affect the system, and in another way the sensors collect the data that will be transmitted over the network and the actuators that allow things to work such as: Humidity sensors provide data to control irrigation systems; Traffic sensors provide data to control traffic lights; Occupancy sensors provide data to control building environments. Sensors and actuators enable IoT solutions in every IoT vertical field from smart cities to smart farming, and from personal health to smart transportation.
There are many different types of sensors in the IoT system. A Brief Overview by Albrecht Schmidt & Kristof Van Learhoven to build a smart device .
4.1. Mobile Phone Based Sensors—MPBS
In the hustle and bustle of modern life, there are many important elements that we cannot live without, and we find ourselves most in need, as they make our life easier and smoother. The smartphone is the pole and element of our life. Dear readers, I think you already have one so you know how important it really is. There is a lot that we can do with a smartphone and many different ways in which it plays a serious role in everyone’s life. You might wonder how this smartphone achieves such wonderful feats. Many of the coolest feats are achieved through various sensors, but do you know how many smartphone sensors and its main function there are in your device? Here are the most important ones:
The Accelerometer sensor detects acceleration, vibration and tilt to determine movement and exact orientation along the three dimensions. Applications use this smart phone sensor to determine whether your phone is in portrait or landscape orientation. It can also tell if your phone screen is facing up word or down ward. The data patterns captured by accelerometer can be used to detect physical activities of the user such as running, walking and bicycling. Figure 3 , illustrate this sensor.
The gyroscope provides orientation details and direction like up-down and left-right but with greater precision like how much the device is tilted. So Gyroscope has the ability of measuring rotation. Hence it can tell how much a smart phone has been rotated and in which direction. Google Sky Map Application use gyroscope sensor to determine the direction towards which your phone is pointed.
Gyroscope sensor is also known as Angular Rate Sensor or Angular Velocity Sensor as shown in Figure 4 . This smart sensor is installed in the applications
Figure 3 . The accelerometer.
Figure 4 . Gyroscope sensor.
where the orientation of the object is difficult to sens by humans. Measured in degrees per second, angular velocity is the change in the rotational angle of the object per unit of time. Furthermore Gyroscope sensor can also measure the motion of the object, so for more robust and accurate motion sensing, Gyroscope sensor is combined with Accelerometer sensor.
Usually known as a compass, it can detect magnetic fields, so the Compass app in smartphones uses this sensor to point to the north pole of the planet. This smart sensor is used in metal detector, and you can find it whenever you open Google Maps or Maps App. The magnetometer is housed in a small electronic chip that often includes another sensor, and is usually built into the accelerometer that helps correct the initial magnetic measurements using tilt information from the auxiliary sensor. Figure 5 shows a module of magnetometer sensor.
GPS speak short of Global Positioning System, units in smart phone communicate with the satellites to determine precisely our location on Earth. The GPS technology does not actually use internet data, this is why once we open the App we can find our location on maps even the offline of Network, but the map itself is blurry as it requires network to load details. GPS is used in all location-based Applications. Accelerometer, gyroscope, magnetometer and GPS work together to create the perfect navigation in your smartphone.
The microphone is basically a sound sensor that detects and measures the loudness of sound. Smartphone generally use micro-sized electret microphones as shown in Figure 6 , because there are so many and diverse type of microphones.
6) Ambient Light Sensor:
The light sensor detects lighting levels in the vicinity to adjust screen brightness accordingly. It is used in automatic brightness adjustment to reduce or increase the brightness of the smartphone screen based on the availability of light, Figure 7 . Dimming the screen on a mobile device also prolongs the lifetime of the battery.
Figure 5 . Magnetometer sensor.
Figure 6 . Microphone sensor.
Figure 7 . Ambient light sensor.
Figure 8 . Touch screen sensor.
7) Touchscreen Sensors:
The smart phone sensors in the touch screen contain an electric current running through it at all times and touching the screen causes a change in the signals. This change is an input to the device. Figure 8 illustrate it. Nowadays, all smartphones use this screen technology. Deeply, the touch screen is responsible for basic input and output operations, and it is used for tapping and writing letters. The touch screen contains three main interaction actions:
i) The main activity and goal of the touchscreen is touching or tapping is defined as the process of clicking on the screen in any place to open, to close or to type a character.
ii) Multi-touch is defined as the process of tapping the screen by more than one finger simultaneously, and this function is usually used in gaming applications.
iii) Gesture is defined as the process of drawing a certain pattern on the touchscreen. Gestures may be implemented with one finger as drag and drop or multi-fingers as in the process of editing photos exactly resizing and changing camera zoom.
8) Fingerprint Sensor:
Gone are the days of saving passwords and all patterns to unlock your phone and now it’s technology time, as many users prefer to use a fingerprint scanner. The fingerprint sensor enables biometric verification to secure many smartphones today. It is a capacitive scanner that records the user’s fingerprint electrically. When you place your finger on its surface, the edges of your fingerprints touch the surface while there is a slight gap between the sockets between the edges. In short, it measures distances and the uneven pattern between the edges on the surface of your finger. This smartphone sensor is very useful in applications that require authentication such as mobile payment applications (ex: Wechat pay).
Moreover, it is the year 2021 of the high tech century, so we don’t have just one, but instead, different types of fingerprint scanners. From the traditional fingerprint scanners prevalent during biometric authentication initiation to today’s related capacitive scanners to the latest ultrasound scanners, however, in reality, the most used type of fingerprint scanner should be the capacitive scanner, currently you will start to see some smartphone manufacturers adopting an all-new ultrasonic fingerprint scanner on their smartphone. Here are the most important types:
i) Optical Fingerprint Scanners: It is obviously from the name suggests, an optical scanner involves the use of optics which is light to capture and scan fingerprints on a device. Essentially, the scanner works by capturing a digital photograph of the fingerprint and then using algorithms to find unique patterns of lines and ridges, spread across the different lighter and darker areas of the image. This image is 2 dimension (2D) depiction of the different patterns of ridges and lines present on the finger and since it comprises of details in the darker sections of the image as well, the same is lit-up using a light source, typically an LED to capture a detailed image. For increasing the level of security, the quality of image is required, because it plays an important role in getting a high definition and detailed image of the fingerprint, which would make it easier to extract more data from the image. Optical fingerprint sensors may be affected by many real-world factors such as stray light, surface pollution, and possibly even a fingerprint left by a previous user. Oil, dirt, scratches on the sensor surface, and condensation are common pollutants that degrade image quality. Figure 9 shows how optical fingerprints work.
ii) Capacitive Fingerprint Scanners: It’s easy to get the idea of involving capacitors in capacitive scanners. However, the definition of a capacitor is an electronic component that stores electrical energy in an electric field. In fact, now you’ll be wondering about its main role in capacitive scanners, it is really important for you to understand that unlike scanners that capture a 2D image of a fingerprint, capacitive scanners capture various details of the fingerprint using only electrical signals. For this purpose, it uses a series of small capacitor circuits, arranged in a matrix, to store the captured fingerprint data. Through the scoring process, a change in protrusions and lines leads to a change in the scoring process, as the charge will be different with respect to the finger placed on the capacitive plate and differ in relation to the air gap between the ridges and the lines. Hence, this change in the capacitor charge is determined using an operational amplifier (AOP) and then recorded with the help of an analog digital converter (ADC). Capacitive sensors can be sensitive to electrostatic discharge but insensitive to ambient light and are more resistant to pollution problems than some optical designs. Figure 10 shows the main function.
iii) Ultrasonic Fingerprint Scanners: This technology is considered the latest
Figure 9 . Optical Fingerprint scanners working way.
Figure 10 . Capacitive Fingerprint Scanners working way.
in fingerprint scanning technology. Unlike optical and capacitive scanners that use light and condenser, the ultrasound scanner uses a high frequency ultrasound. The process involves the use of an ultrasound pulse, which is sent through an ultrasound transmitter toward the finger that rests on the scanner. This impulse immediately strikes the finger, part of it moves, while part of it is reflected back. This last pulse is then captured by an ultrasound receiver, which depends on the pulse’s intensity, and captures a 3D image of the fingerprint. This change causes the intensity of the pulse in the finger tissue that forms the bumps and lines. Ultrasound fingerprint scanners have the advantage of being able to see under the skin. Not only does this provide live finger verification, but it provides more information as a biometric. Figure 11 shows ultrasonic.
9) Heart Rate Sensor:
The heart rate sensor measures the heartbeat with the help of optical sensors and LED lights. An LED light is emitted towards the skin and this smartphone sensor detects the light waves that are reflected on it. There is a difference in the intensity of the light when there is a pulse. Heart rhythm is measured by calculating changes in the intensity of light between minute pulses of the blood vessels. Many fitness and health apps use this method to calculate your heart rate.
10) Barcode/QR Code Sensor:
Most smartphones have barcode sensors that can read a barcode by detecting the light reflected from the code. It generates an analog signal with a variable voltage representing the barcode. Then this analog signal is converted into a digital signal and finally is decoded to reveal the information in it. Barcode sensors are useful for scanning barcode or QR code products. It is used in most social media applications and very useful in payment process.
4.2. Medical Sensors
The Internet of Things plays a more important role in medical technology with the goal of making medical devices more effective and safer, while simplifying their operations. The Internet of Things is expanding the medical field with many
Figure 11 . Ultrasonic fingerprint scanners.
applications based on smart sensors, which monitor the health of a patient when he is not in the hospital or when he is alone. After that, they can provide immediate feedback to the doctor, relatives, or patient. In the medical market, you can find many wearable sensors available as shown in Figure 12 . They are equipped with medical sensors that are able to diagnose the patient and measure various parameters like heart rate, respiratory rate, blood pressure, blood sugar levels, and body temperature .
The Internet of Things is striving to obtain its new device that surpassed the smart devices that the user wears, this device to detect spots that are affixed to the skin, such tattoos on the skin are disposable, stretchy and very cheap.. The electronic fitting is rubber housing. The patient is supposed to wear these pads for a few days to monitor a vital health laboratory continuously .
4.3. Specialized Physical, Mechanical, and Chemical Sensors
Touch, temperature, or air pressure sensors can be combined for use in more specialized applications. For certain mobile work settings, more sensors such as gas concentration and radiation sensors can be added to increase the user’s perceptual capabilities and facilitate automatic information capture.
4.4. Radio Frequency Identification (RFID)
RFID (Radio Frequency Identification)  is a form of radio communication that involves the use of electromagnetic or electrostatic coupling in the radio frequency portion of the electromagnetic spectrum to uniquely identify an object, animal, or person. RFID technology use cases include healthcare, manufacturing, inventory management, shipping, retail sales, and home use. There are two common types of RFID. First, Active RFID tags contain the transmitter and power supply (battery) on board the tag. These are mostly UHF solutions, and reading ranges can extend up to 100 meters in some cases. Second, Passive RFID solutions, the reader and reader antenna send a signal to the tag, and this signal is used to power the tag and reverse the power back to the reader. There are negative LF, HF and UHF systems. The reading ranges are shorter than the active tags and are limited by the strength of the radio signal that is reflected back to the reader (tag back-scatter). Figure 13 shows a module of RFID.
Figure 12 . Smart watches and fitness trackers in left, Embedded skin patch in right.
Figure 13 . Radio frequency identification.
Actuators are mechanical or electro-mechanical devices that provide controlled and sometimes limited movements or positioning that are actuated electrically, manually or by various fluids such as air, hydraulic, etc. Linear actuators convert power into linear motions, usually for positioning applications (Electric and Hydraulic). A hydraulic actuator consists of a cylinder or fluid drive that uses hydraulic power to facilitate mechanical operation. Mechanical movement gives an output in terms of linear, rotational, or oscillatory motion. Just as it is almost impossible to compress a fluid, a hydraulic actuator can exert great force. The disadvantage of this approach is its limited acceleration. Electric actuators use electrical energy. The electric actuator may provide operating power/torque in one of several ways. Electromechanical actuators can be used to drive a motor that converts electrical energy into mechanical torque. The other path is elector-hydraulic actuators, where the electric motor remains the main drive and furthermore provides torque to drive a hydraulic collector which is then used to transmit drive power in the same way that a diesel engine/hydraulic component is normally used in heavy equipment.
Rotary actuators convert energy to provide rotational motion (Pneumatic). Pneumatic actuators use compressed air. Pneumatic actuators also allow large forces to be produced from relatively small pressure changes. A pneumatic actuator converts the energy formed by the vacuum or compressed air at high pressure into linear or rotational motion. The pneumatic power is eligible for main motor control because it can respond quickly at start and stop as the power supply does not need backup storage for operation. Pneumatic actuators are safer, cheaper, more reliable, and more powerful than other motors. Figure 14 shows an example of water pump.
5. Main Issues and Challenges of IoT
The sharing of IoT based systems in all aspects of human lives and the various technologies involved in transferring data between embedded devices made it complex and led to many problems and challenges. These include security; privacy;
Figure 14 . Example of an actuator (pump water).
interoperability and standards; legal, regulatory, and rights; and emerging economies and development.
While security considerations are not new in the IT context, the features of many IoT applications present new and unique security challenges. Facing these challenges and ensuring security in IoT products and services should be a primary priority, and users need to trust that IoT devices and related data services are protected from vulnerabilities, especially as this technology has become more pervasive and integrated in our daily lives. Poorly secured IoT devices and services can act as potential entry points for a cyber attack and expose user data to theft by leaving data flow insufficiently protected . The interconnected nature of IoT devices means that every poorly secured device connected to the Internet has the potential to affect Internet security and resiliency globally. This challenge is amplified by other considerations such as the widespread deployment of homogeneous IoT devices, the ability of some devices to automatically connect to others, and the potential for deploying these devices in insecure environments .
The full potential of the Internet of Things depends on strategies that respect individual privacy options across a wide range of expectations. The data flows and user privacy that IoT devices provide can open up incredible and unique value for IoT users, but concerns about privacy and potential harms may hinder the full adoption of IoT. This means that privacy rights and respect for user privacy expectations are integral to ensuring user confidence in the Internet, connected devices, and related services .
5.3. Interoperability and Standards
Interoperability is the ability to exchange information between various IoT devices and systems. This exchange of information is not based on published software and hardware. The problem of interoperability arises due to the heterogeneous nature of the technology and the various solutions used to develop IoT. The four levels of interoperability are technical, semantic, syntactic, and organizational . With interoperability as an important issue, researchers have agreed with several solutions such as adaptive, gateway based, virtual network and service based architecture. They are also known as approaches to dealing with interoperability . Although the methods of dealing with interoperability relieve some pressure on IoT systems, there are still some challenges that remain with the possibility of interoperability which could be an area for future studies .
5.4. Legal, Regulatory, and Rights
The use of IoT devices raises many new regulatory and legal questions in addition to amplifying existing legal issues around the Internet. The questions are wide-ranging, and the rapid rate of change in IoT technology often outpaces the adaptability of associated policies and legal and regulatory structures. With the development of the Internet of Things, many real-life problems have been solved but have also given rise to critical ethical and legal challenges such as data security, privacy protection, trust, security, and data usability . It has also been observed that the majority of IoT users support government rules and regulations regarding data protection, privacy and safety due to mistrust of IoT devices. Therefore, this issue should be taken into consideration to maintain and improve trust among people regarding the use of IoT devices and systems.
5.5. Emerging Economies and Development
The Internet of Things holds great promise to deliver social and economic benefits to emerging and developing economies. This includes areas such as sustainable agriculture, water quality and use, health care, manufacturing, and environmental management, among others. As such, the Internet of Things holds promise as a tool for achieving the United Nations Sustainable Development Goals .
The Internet of Things consists of many smart devices that communicate with each other. These devices enable data exchange and collection. Smart devices can have a wired or wireless connection. Typically, IoT devices connect to the Internet through the Internet Protocol (IP) stack. This combination is very complex and requires a large amount of power and memory from the connected devices. These devices can also be connected locally through NON-IP networks which consume less power and connect to internet via smart gateway .
6.1. Device-to-Device Communications
A device-to-device communication model represents two or more devices that directly communicate and communicate with each other, rather than an intermediary application server. These devices communicate over many types of networks, including IP and The internet. However, these devices use protocols like Bluetooth, Z-Wave or ZigBee to create direct device-to-device connections . Figure 15 shows this direct connection of a real example.
Device-to-device networks allow these devices that committed to a specific connection a protocol for communication and exchange messages to achieve their task. This is the contact form is commonly used in applications such as home automation systems. Usually, small data packets are used information for communicating between devices with relatively low data rate requirements.
6.2. Device-to-Cloud Communications
In the device-to-cloud communication model, IoT device connects directly to the internet cloud. A service like an application service provides data exchange and message movement control. This approach often takes advantage of the menu communication mechanisms like traditional Ethernet or Wi-Fi wired connections to create connection between device and IP network, which eventually connects to the cloud services as illustrated in Figure 16 .
6.3. Device-to-Gateway Communications
In the device-to-gateway model, the device layer gateway to the application (ALG). The IoT device communicates through an ALG serving as a channel to access the cloud services. Simply, this means that there is an application program running on a local gateway device which acts as an intermediary between device and cloud service and provides security and data translation. The form is shown in Figure 17 .
In most cases, a smartphone with an application to communicate with a device
Figure 15 . Example of device-to-device communication model.
Figure 16 . Example of device-to-cloud communication model.
acts as a local gateway and transfers the data to a cloud service. Devices like a fitness tracker are unable to connect directly to the cloud. Hence, they rely on smart phone applications to transfer data to the cloud.
6.4. Back-End Data-Sharing Model
The back-End data sharing model refers to a communication architecture that enables users to export and analyze smart object data from a cloud service in combination with data from other sources. The data is then uploaded to two different application service providers. The architecture also helps with data collection and analysis. For example, an industrialist is interested in analyzing the energy consumption of the plant by collecting the data produced by the IoT sensors and utility systems.
The back-end data sharing model suggests unified cloud services  or cloud approach application programmer interfaces (APIs) are necessary to achieve smart interoperability Cloud-hosted device data . Figure 18 presents this model.
Figure 17 . Example of device-to-gateway model.
Figure 18 . Example of Back-End data sharing model.
7. Applications of IoT
This new wave of technology will stand at the leading position for all technologies around the world, which are directed towards billions and billions of connected smart devices that use all the data in our lives. With new wireless networks, high sensors, and superior capabilities, IoT applications promise to make our lives easier and bring enormous value. Some uses of IoT applications are found in several important areas. The following application areas are the top for 2020 analysis .
7.1. Area 1: Manufacturing/Industrial
The IoT industrial application area covers a wide range connection of objects to objects, projects from inside and outside. For the inside, many IoT-based factory automation and control projects include comprehensive smart factory solutions with many elements such as production floor monitoring, wearable devices and augmented reality in the shop floor, remote PLC or automated quality control systems. Typical off-plant projects include remote control of connected machines, and equipment monitoring. Several case studies indicate that the main drivers for OEMs to provide IoT solutions are “reduced downtime and cost savings”.
Autonomous vehicles: Free-roaming robots move across factory floors: Nowadays with the convergence of technologies like robotics, sensors, 3D cameras, 5G connectivity, software and artificial intelligence, swarms of autonomous vehicles having found their way safely onto factory floors as a means of increasing the speed and accuracy of routine operation. These free-roaming robots can be coordinated to a greater extent than ever before, enabling them to perform automated tasks in a controllable and predictable manner and with minimum human oversight. This gives them the potential to improve operations inside manufacturing plants, especially in areas such as component handling and transportation, offering opportunities to increase productivity, reduce risk, decrease cost and improve data collection. This liberates workers to focus their attention on higher value activities such as production and assembly. For example, an Italian manufacturer namely Automotive
Systems Manufacturer Faurecia (ASMF) is using autonomous vehicles from Mobile Industrial Robots to increase the efficiency of its logistics .
Machine utilisation: Making the most of industrial assets: The IoT architecture has emerged as a popular and powerful way to monitor machine usage, sending valuable performance data to operators via dashboards to inform them of machines that are running more efficiently compared to other equipment. These platforms can act as a major driver in improving factory floor production, primarily by eliminating bottlenecks due to low-performing assets. They can also be used to compare the performance of devices across one or more sites .
7.2. Area 2: Transportation/Mobility
Maintaining vehicle health: Predictive maintenance technology relies on the use of Internet of Things (IoT) communication tools that collect data about the performance of different parts, transfer that data to the cloud in real time and assess the risk of a possible malfunction of the vehicle’s hardware or software. After the information is processed, the driver is notified and informed of any service or repair necessary to avoid potential accidents. With Internet of Things connectivity tools, you can forget about unplanned stops or breakdowns during the ride .
Transforming the meaning of vehicle ownership: One of the most interesting future applications of the IoT in transportation is vehicle ownership. According to a recent study by Tony and James , car ownership will decrease by 80% by 2030. You can see that actually happen. City dwellers sell or never buy cars. They choose to use ride-sharing and vehicle-sharing platforms or ride-sharing like Uber, DiDi and Alibaba bikes, in addition to relying on steadily improving public transportation services.
7.3. Area 3: Energy
With energy consumption worldwide expected to grow by 40% over the next 25 years, the need for a smarter energy solution has reached an all-time high. Fortunately, there are some major shifts towards more efficient energy management from smart light bulbs to fully autonomous offshore oil platforms. Overall, IoT is revolutionizing nearly every part of the energy industry from generation to transmission to distribution and changing how energy companies and customers interact. It is difficult to underestimate the current impact of the Internet of Things on the energy sector. With the increasing demand for process automation and operational efficiency, more companies are exploring IoT use cases in energy management.
Energy system monitoring and maintenance: IoT can be used in the energy industry to track a number of system metrics, including overall health, performance, and efficiency. As a result, their maintenance is simplified. Whether it’s a wind turbine, solar panels, or other important equipment, it can be difficult to pinpoint a problem before the system crashes. Moreover, checking for issues manually is a very waste and tedious process.
Increased efficiency: Improving the efficiency of coal-fired plants is one of the most difficult challenges in the electricity industry today. Plant technology is usually more mature, systems are very complex and average efficiency rates are low. General Electric today released a new digital energy program designed to play a significant role in helping countries meet COP21 greenhouse gas emissions targets. Using IoT helps increase the efficiency of a coal power plant by up to 16%, while reducing greenhouse gas emissions by 3%. This is achieved by improving fuel combustion and adjusting the process according to the characteristics of the fuel being burned.
Safety and disaster prevention: IoT solutions can also be used in the energy industry to improve operational safety and prevent production accidents, as well as eliminate their consequences, such as Safety drones  that can be used as part of a risk management system to reduce employee risks at nuclear plants or on mining sites.
Smart meters: These IoT power devices connect consumers directly to the power distribution station, allowing two-way communication. Thus, they can send critical operating information to utility agencies in real time. So this direct connection helps utility agencies to quickly address any performance issues, including outages and reduce system downtime. Smart meters can automatically identify and isolate the damaged portion of the line without affecting the performance of the rest of the network. In general, specialized companies find that there are many ways in which consumers can benefit from the use of smart meters.
7.4. Area 4: Retail
More and more retailers are realizing that they can improve their cost efficiency and in-store customer experience through innovative IoT use cases. There is an increase for retailers to digitize stores and create smarter operations. Retail now accounts for 9% of selected projects, up from 5%. Typical IoT in retail solutions include in-store digital signage, customer tracking and engagement, merchandise control, inventory management, smart vending machines and more .
7.5. Area 5: Smart Cities
Thanks to the power of the Internet of Things, entire cities are becoming digitally interconnected and thus smarter. By collecting and analyzing huge amounts of data from IoT devices across different city systems, cities improve the lives of citizens. Smart cities can make better decisions through the data they collect on infrastructure needs, transportation requirements, crime and safety. A study shows that using existing smart city applications, cities improve quality of life indicators (such as crime, traffic, and pollution) by between 10% and 30%. Internet of Things technologies in everyday life as part of your home, transportation, or city, relate to a more efficient and enjoyable life experience. IoT promises a better quality of life through routine chores and increased health and wellness .
7.6. Area 6: Healthcare
The Internet of Things has only slowly spread in healthcare. However, things seem to be changing in light of the epicenter of the COVID-19 pandemic. Early data indicates that digital health solutions related to COVID-19 are on the rise. Demand is increasing for specific IoT health applications such as telehealth consulting, digital diagnostics, remote monitoring, and robotic assistance. IoT Healthcare apps help with; Reduced waiting time for the emergency room; Track patients, employees, and inventory; Strengthening drug management; Ensure the availability of important devices. IoT has also introduced several wearables and devices which has made lives of patients and doctors comfortable. Please check this work  for more details.
7.7. Area 7: Supply Chain
As supply chains extend more and more to end customers, resulting in more complex flows of goods whose delivery is more complex, logistics service providers are increasingly integrating connected digital solutions to address complexity. In the supply chain, Internet of Things devices are an effective way to track and authenticate products and shipments using GPS and other technologies. In addition, they can also monitor product storage conditions which enhance quality management throughout the supply chain. IoT devices revolutionized supply chain management. It is easier to understand where the goods are and how they are also stored when they can be expected at a specific location. Here are the major benefits: 1) Authenticate the Location of Goods at any time. 2) Track Speed of Movement and when Goods will arrive. 3) Monitor Storage Conditions of Raw Materials and Products. 4) Streamline the Problematic Movement of Goods. 5) Locate Goods in Storage. 6) Administer Goods Immediately Upon Receipt .
7.8. Area 8: Agriculture
The current world population is 7.8 billion in 2020 and it is expected to reach 8.6 billion in 2030, 10 billion in 2050 and 11.2 billion in 2100, according to the most recent United Nations estimates elaborated by Worldometer . Just imagine how you can feed such a massive population with a simple agriculture. So to feed this huge population required to marry agriculture to technology and obtain good results.
A smart greenhouse is one of many possibilities that exist for solving this problem. The smart greenhouse is a revolution in agriculture, creating a self-regulating microclimate suitable for plant growth through the use of sensors, motors, and monitoring and control systems that improve growing conditions and automate the growth process .
7.9. Area 9: Buildings
Majority believe that smart buildings will provide greater connectivity in building systems. Certainly, buildings contain complex mechanical HVAC systems as well as control systems that can improve the comfort and productivity of building occupants. Therefore, smart building technology can provide the means to achieve higher levels of integration between existing building systems. This is expected to increase as open standards continually pave the way. However, smart building technology will go beyond those concepts. Smart buildings have an amazing ability to connect people with technology. Not only will smart building technology assist in the facility management effort, but smart building technology will also provide valuable insights for the use and enjoyment of building spaces. It will benefit energy efficiency, building sustainability, and workforce management efforts. Smart house, also called home automation is the trend in this area .
IoT has gradually brought about a lot of technological changes in our daily life, which in turn helps make our lives simpler and more comfortable, through various technologies and applications. There is an infinite benefit to IoT applications in all fields. The Internet of Things holds an important promise to provide social and economic benefits to the emerging and developing economy. This includes areas such as sustainable agriculture, water quality and use, health care, manufacturing and environmental management, among others. As such, the IoT holds promise as a tool in achieving the United Nations Sustainable Development Goals. However, the issues and challenges associated with IoT must be considered and addressed in order to realize the potential benefits to individuals, society and the economy.
Ultimately, solutions will not be found to maximize the benefits of the IoT while minimizing the risks by engaging in a polarized discussion that pits IoT’s promises against its potential risks. In a way, it will take informed participation, dialogue and collaboration across a range of stakeholders to chart the most effective way forward, and the set of IoT challenges will not be limited to industrialized countries. Developing regions will also need to respond to realize the potential benefits of the Internet of Things. In addition, it will need to address unique needs and challenges for implementation in less developed regions, including infrastructure readiness, market and investment incentives, technical skills requirements, and policy resources.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.
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- 1 IoT Research Lab, ECSE, Ontario Tech University, Oshawa, ON, Canada
- 2 Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Parkville, VIC, Australia
The Internet of Things (IoT) is a conceptual paradigm that connects billions of Internet-enabled devices to exchange data among themselves and their surroundings to enable smart interactions and connect the physical infrastructure to digital systems. IoT represents a revolutionary paradigm that started to affect our lives in many positive ways. The term Internet of Things was first coined in 1999 by Kevien Ashton ( Ashton, 2009 ) and was initially designed to support RFID technology. However, nowadays IoT has reached far beyond its designers’ vision and become much popular for the new applications it opens up in many vital domains like healthcare, intelligent transportation, public safety, home automation, smart city, asset monitoring, industrial automation and much more. The evolution of IoT presented the long-awaited promise of ubiquitous data access in which people wanted to have access to real-time data on the go anywhere and anytime.
Even though there are many other relevant paradigms/model that intersect with the purpose of IoT (e.g., M2M: Machine to Machine), Web of Things, Internet of Everything (IoE), pervasive computing, etc. ), there are fundamental differences between them and IoT. The core values of IoT lies in the promise of helping businesses to increase their productivity, enhance control over their assets, and make informed business decisions based on the inference resulting from the processing of the fusion of big raw data acquired from the surroundings, including people themselves. Recent research statistics reveal over 10 billion connected IoT devices in 2021. This number is anticipated to reach 41 billion in 2027, expecting over 152,000 IoT devices to connect to the Internet per minute in 2025. Considering the global IoT market size, there was a 22% increase in the market size of IoT in 2021, hitting $157.9 billion. Smart home devices are the dominant components of IoT. The penetration rate of IoT varies concerning the application domain. For example, IoT analytics ( Lueth, 2020 ) argues that industrial applications occupy 22% of the global IoT projects, with transportation, energy, and healthcare occupying 15%, 14%, and 9%, respectively.
The main two types of devices that make up the most of IoT are: Sensors and Actuators. Sensors are physical devices that can sense/measure a certain phenomena and can communicate the sensed values to other parties (i.e., collect data and report internal states). A GPS and an ECG device are examples of IoT sensors. The constituent sensor nodes usually utilize small-scale embedded systems to achieve the cost-effectiveness criteria of IoT solutions, increasing their deployment in various domains. Sensor nodes often use 8-bit microcontrollers and inhibit small storage capacity, lowering their power sizing and allowing them to run for years on batteries. Coupled with the diversified networking protocols available to match the existing infrastructure or the operational conditions, this highly promotes the deployment of IoT solutions in different domains. Actuators are also physical devices that can affect a change on the physical environments (i.e., take actions) in response to a command or a recommendation such as an AC thermostat and a valve. These devices need to be connected to the Internet and are able to communicate to send or receive data so they can qualify as IoT devices.
The convergence of IoT, advanced data analytics and artificial intelligence opened up the door for the next generation of applications that support real-time decision making such as improved user experience and predictive maintenance. As such, data analytics has become a core component of any IoT deployment and will continue to gain popularity and relevance to businesses as much as data collection continues to grow and support intelligent decision making. In industrial manufacturing, for example, predictive maintenance can predict when maintenance is required in advance through the measurement of vibration levels, heat and other parameters to avoid production disruption. IoT data can also reveal rich information about customer behaviors (e.g., driving habits and shopping preferences) to support improved customer experience. Machine learning models and artificial intelligence techniques can learn from observations (IoT data collection) and recommend actions that lead into smart decisions (IoT actuation).
Although IoT promises to support intelligence decision making, enable better quality of life to citizens and make transformative changes in their daily lives, there remain grand challenges that hinder IoT from reaching its full potential such as privacy and security concerns, data heterogeneity and device interoperability, unrestricted access control and deployment in the open access domain. The heterogeneity and small footprint of IoT of sensors for example, comes with two major shortcomings: 1) The constraints of resources available on the sensor nodes render it infeasible to apply the conventional security mechanisms typically involved in capable computer systems, exposing the sensor nodes as a weak security point for the whole IoT system. 2) The many networking protocols available to communicate sensed information among IoT devices result in interoperability issues between IoT systems utilizing different communication protocols.
The first shortcoming of incapable sensor nodes results in the notorious “vertical silos,” where an IoT system is, in fact, a set of subsystems that lack information sharing among each other. That did not represent a significant concern at the early ages of IoT since the applications were relatively limited, and the IoT had not reached its maturity and big vision yet. However, the advent of cloud computing in the last decade, coupled with the advancements in artificial intelligence and its subdomains, has vowed the prospect of IoT in various domains. This necessitates the ability of collaborative IoT systems to build better-informed business decisions based on the fusion of inferences coming from multiple systems. However, the second shortcoming of IoT impedes the scalability of IoT systems, confining the usability of sensed information by IoT systems to the managed networks of their users without exposing this information to public networks. This comes at the cost of increased IoT systems outlay, unwanted redundancy of the same information sensed by non-interoperable systems, expanded storage footprint, high network bandwidth utilization, risen processing cost, and more elevated system latency. This paper provides deep analytical views on many aspects of IoT technologies including standard architecture, stack protocols, value proposition, different IoT applications, trending technologies, and challenges.
The rest of the paper is structured as follows. Section 2 discusses the IoT standard architecture, enabling technologies and stack protocols. Section 3 describes the different domains of application for IoT with the most prevailing deployments. Section 4 sheds the light on rising trends in IoT and the convergence between IoT and data analytics. Section 5 discusses the grand challenges for IoT that remain open for further research and deemed to decelerate its wide scale adoption. Lastly, Section 6 offers concluding remarks.
2 IoT standard layered architecture and protocols
From the engineering perspective, IoT is witnessing an increasing number of enabling technologies. This high diversity of IoT enabling technologies stem from the proliferation of IoT devices, their heterogeneity and uncertainty of operational environments, the advancements in chip manufacturing, and variety of communication protocols ( Bouguettaya et al., 2021 ). Nonetheless, the advent of artificial intelligence (AI) and associated machine learning (ML) techniques leverage the serendipity of IoT by providing insightful information from the fusion of raw data collected by heterogeneous sensors to support decision making and change how people carry out their everyday business. This adds up to the enabling technologies of IoT. Therefore, abstracting IoT systems in terms of building blocks helps to contrast the hazy boundaries between different enabling technologies and enhance the agility and robustness to achieve a successful paradigm for IoT systems ( Lin et al., 2017 ). The core elements of a typical layered IoT architecture, as depicted on Figure 1 , can be summarized as follows.
FIGURE 1 . IoT layered architecture.
2.1 Perception layer
The first layer of the IoT architecture is the perception layer, also denoted by the hardware, physical, or infrastructure layer. This layer encompasses the constituent physical devices of an IoT system that are typically responsible for: 1) sensing the environment in their vicinity and sending the raw sensed data to the next upper layer for processing, such as environmental sensors; 2) transforming the logical decisions coming from the upper inference layer into physical actions applied to corresponding devices, such as actuators and servo motors. It is worth noting that, and as the name of IoT implies, the constituent devices that form an IoT system embed some form of communication by which they can be directly or indirectly, with the help of a gateway, connected to the Internet. Moreover, IoT devices typically include some form of identification that helps differentiate the data passed to the upper layers of the IoT architecture. This identification can be either burnt into the device firmware by the manufacturer [such as the unique identifier (UUID)], set up by the user through configurable menus or DIP switches, or provided by the communication subsystem that the devices utilize (like the MAC address or the Bluetooth identifier).
2.2 Transport layer
The transport layer, also denoted by the communication and network layer, and as its name implies, is responsible for connecting IoT devices in the perception layer to the upper layers of the IoT architecture, which are typically hosted over the Internet using cloud computing technologies. This layer utilizes a wide range of communication technologies, like cellular, Wi-Fi, Bluetooth, Zigbee, etc. Besides, the transport layer is responsible for maintaining the confidentiality of the data exchange between the perception layer and the upper layers. Nonetheless, with its potential promise and anticipated ubiquity and prevalence, IoT is the motivating force behind recent research in enabling communication technologies. For example, IPv6 has been identified such that it can provide network addresses to the anticipated enormous smart objects connected to the Internet, which exceeds the already depleted IPv4 addresses. Similarly, the 6LoWPAN communication standard has been mainly developed to enable IPv6 packet transmission for power-constrained smart objects communicating over IEEE 802.15.4.
The transport layer securities typically used for IP-based networking, namely Transport Layer Security (TLS) and Datagram TLS (DTLS), provide the essential means for secure end-to-end communication. However, these technologies are not always feasible for deployment in resource-constrained embedded IoT devices due to the induced increased processing, storage, and power consumption overhead associated with these security mechanisms. This, in turn, usually delegates the authentication and the data integrity tasks of exchanged information in IoT systems to be arbitrarily carried out by the application layer based on the required security level and device capabilities. Besides, it exposes these poorly-secured IoT devices as a weak point for malicious users to penetrate the underlying critical network infrastructure or exploit them for botnet attacks to prevent the availability of network resources, aka distributed denial of service (DDoS). Derived by the proliferation of IoT devices, recent statistics anticipate that more than %25 of all cyberattacks against businesses will be IoT-based by 2025. This slows down the adoption of IoT and makes businesses reluctant to expose the reachability of sensed information by their IoT systems beyond their managed networks, adding up to the “isolated islands” dilemma of IoT systems.
2.3 Processing layer
The processing layer, also denoted as the middleware layer, encompasses advanced features that could not be embedded within the inherently resource-constrained devices at the perception layer. This includes storage, processing, computing, and action-taking capabilities. Besides, the middleware layer facilitates IoT systems’ scalability and interoperability across the computing continuum from the edge to a remote cloud data centre. It typically provides interfaces, like APIs, for other systems and third-party services to leverage the gathered raw data from the IoT devices or the insight obtained by the middleware layer after data processing. Based on the agreed tradeoff between device loads and bandwidth during the system design phase, the middleware layer can be either embedded within an on-site capable embedded platform, sometimes denoted as an IoT gateway, or hosted over the cloud. The former requires utilizing a medium-to-large scale embedded device to act as a gateway. Nonetheless, it typically utilizes a Linux kernel-based OS to mask the complexity of the underlying hardware interfacing to the perception layer devices. The latter, however, depends on relaying the raw data from the perception layer to cloud-hosted servers. This comes at the cost of higher bandwidth utilization and increased latency.
The emergence of a cloud-hosted middleware layer for IoT systems represents a bottleneck considering the security concerns of IoT. Cloud computing is the only candidate to digest the enormous amount of IoT data coming from perception layer devices. However, cloud providers are also principal targets for cyberattacks and single points of failure for IoT systems. A successful cyberattack could expose an enormous amount of sensitive information to hackers and render the IoT system unfunctional. This puts system designers in a tradeoff of choosing between the capability, cost-effectiveness, and ease of access associated with cloud computing technologies on one hand and the panic of potential data leakage in case of a successful cloud attack on the other hand.
2.4 Application layer
The application layer defines the domains by which IoT systems are deployed. This includes smart homes, smart cities, smart agriculture, etc. The application layer manages the logical processes to be taken based on the inference coming from the middleware layer and the system requirements. This includes sending emails, activating alarms, turning a device on or off, setting parameters, etc. Therefore, the application layer represents the user interface to interact with the other layers below it, facilitating human-machine interactions. Since the application layer is meant to be used by people, it inhibits a wide surface area exposed to good actors and bad actors. The common vulnerabilities usually encountered in the application layer include distributed denial of service (DDoS), HTTP floods, SQL injections, and cross-site scripting. Although large-scale cybersecurity attacks are dangerous, the effect of small-scale cybersecurity attacks, usually encountered in IoT systems, can be even more dangerous. This is because they do not have unique ecosystems, their cyber defense has not yet reached maturity, and they can be gone unnoticed for a long time. The security mechanisms applied at the application layer are meant to fulfill the CIA triad, namely confidentiality, integrity, and availability. This implies keeping the secrecy of exchanged information between communicating parties, ensuring that no alterations have been maliciously carried out on the information from its source to destination, and making the information always available to authorized users requiring it.
In contrast to the vulnerabilities in the lower layers of the IoT architecture mentioned above which can also affect the upper layers, security breaches in the application layer do not affect the lower layers. In capable computer systems, however, security mechanisms are applied in parallel to different layers to tighten the system’s safety. Nevertheless, for a market usually biased towards the price and the convenience rather than the security, this is not usually valid for constrained embedded devices often encountered in IoT systems. Security practices for IoT systems often delegate the security measures to be applied at the application layer based on the system requirements or even delegated to the third-party firewall appliances managing the network. Security measures in IoT systems usually come at trade-offs regarding the capacity of the constituent IoT devices utilized by the system. Moreover, with the diverse application domains of IoT, security mechanisms can even affect the system’s effectiveness. For example, a VoIP-based IoT solution can be adversely affected by the induced latency of the security mechanism in action. On the other hand, this latency pales for confidentiality- and integrity-critical applications, like financial and medical applications, where the effect of a security breach would be catastrophic.
2.5 Business intelligence layer
A successful IoT system depends on the utilized enabling technologies and how inference is delivered to the user abstractly and efficiently. The business intelligence layer is meant to fulfill this task by providing the user with visualized representations of the information coming from the middleware layer, masking its complexity and making it easier for the user to make informed business decisions.
The business intelligence layer is not affected by the constituent embedded IoT devices utilized by the system. It does not deal directly with the constituent IoT devices. However, it deals with the inference from the middleware layer after processing the raw data from the IoT devices through the application layer protocols. Therefore, the security of the business intelligence layer depends on the typical user-level security mechanisms found in capable computer systems. The user-level security can be applied to different entities constructing the layer. This includes files, databases, or any other resources. The user-level security is meant to implement a fine-grained authorization control over accessible information to different users based on their credibility.
3 IoT applications
IoT can be seen in different real-world applications and services such as home automation, intelligent transportation, smart cities, digital healthcare, remote health monitoring, smart agriculture, and industrial automation ( Gubbi et al., 2013 ). In each application domain, several sensors are triggered to independently gather data, transmit information, and initiate and execute services with minimum human intervention ( Sarkar et al., 2014 ). The main objective of integrating IoT technology into real-world applications is to enhance the quality of life. For example, in the domain of smart city services, we find IoT applications used for increasing city safety, efficient mobility, and enhancing smart energy usage ( Weber and Podnar Žarko, 2019 ). On the other hand, IoT technology has introduced remote medical monitoring systems in the healthcare domain which empower physicians to provide superior care to patients ( Selvaraj and Sundaravaradhan, 2020 ). Numerous research proposes different IoT-based solutions and innovations under three application domains shown in Figure 2 ( Sarkar et al., 2014 ).
FIGURE 2 . IoT application domains and related services.
3.1 Smart city
IoT technology assists cities to enhance mobility services, improve public safety, and control and automate household systems. Intelligent transportation, for example, focuses on solutions that manage road infrastructure and improve route planning for drivers. Furthermore, it provides innovative solutions to monitor and manage traffic systems using smart traffic signals and sensors, throughout the road network to smooth the traffic flow and reduce congestion. The concept of smart city services is not restricted to transportation, but also involves other aspects of human life, such as public safety, green and clean environment, smart grid, efficient delivery of municipal services and connecting the physical infrastructure to the digital world. In the following we shed the light on some of these aspects.
3.1.1 Traffic management
IoT-based traffic management systems mainly monitor road traffic conditions to solve the problem of increased traffic congestion and predict traffic status ( Poslad et al., 2015 ). These systems assist drivers by informing them about the traffic conditions at a given location and time. For example, an adaptive traffic signal control (ATSC) system that captures the traffic volume level can significantly reduce traffic congestion ( Saarika et al., 2017 ). Equipping roads with advanced sensors that capture real-time traffic data assists in determining the duration of traffic light signals across intersections. The ATSC system not only eases the traffic flow at intersections but also reduces travel time and fuel consumption, contributing positively to green environments. Research on IoT-based real-time ATSC systems at an intersection describes the coordinated approach as it is used to track the movements of vehicles and pedestrians ( Eom and Kim, 2020 ; Jamil et al., 2020 ). It also uses the deep reinforcement learning (DRL) method which is commonly used in ATSC systems to teach/educate traffic controllers how to make proper decisions. DRL simulates the effect of a traffic signal’s action and the resulting changes in traffic status. It would be classified as an appropriate action in that given situation if the action improved traffic conditions, and as a negative action otherwise ( Jamil et al., 2020 ). Furthermore, IoT-based traffic management systems can facilitate smart parking in public spaces such as on-street parking, and lot parking. According to Libelium company ( Dujić Rodić et al., 2020 ), parking spaces play critical roles in reducing traffic volume, and gas emissions. In smart parking, drivers can easily locate an empty parking place using smart parking maps. These smart maps use IoT sensors and cameras to detect and manage the likelihood of parking space in a given area. An example of a real-time traffic occupancy system for smart parking is called SplitParking which is managed by the city of Split in Croatia ( Weber and Podnar Žarko, 2019 ). The SplitParking system places sensors integrated with an IoT technology within its parking spaces to monitor space occupancy andlert the end user of the parking availability through a user-friendly mobile application ( Weber and Podnar Žarko, 2019 ).
3.1.2 Intelligent transportation
The emergence of IoT has provided a new perspective for intelligent traffic systems development. This is because the IoT paradigm satisfies the public’s demand towards an “always connected” model by relying on the interconnection of our daily physical objects using the Internet. Hence, allowing it to collect, process and transfer data creating smart intelligent systems without human intervention.
In the traffic domain, IoT requires every element such as roads, tunnels, bridges, traffic lights, vehicles and roadside infrastructures to be Internet-connected for identification and management purposes. This can be done using sensor-enabled devices, for instance, RFID devices, Infrared sensors, GPS and many others. Intelligent traffic systems that are IoT-based can efficiently improve traffic conditions, reduce traffic congestion and are unaffected by weather conditions. Moreover, IoT allows for dynamic real-time interactions, since it facilitates the incorporation of communication, control and data processing across the transportation systems. Beyond any doubt, IoT is causing a noticeable shift in the transportation sector.
The rapid advancements within information and communication technologies have also paved the way for developing more self-reliable and intelligent transportation systems. These include striding advancements in hardware, software, sensor-enabled and wireless communications technologies. Therefore, moving towards a new era of connected intelligent transportation systems, where the demand for on-going and future real-time traffic data continues to rise ( Abdelkader and Elgazzar, 2020 ). This enforces several challenging requirements on the traffic information systems. Among these requirements is broadcasting real-time, user-friendly, and precise traffic data for users. These traffic data including color-coded maps showing congestion, calculated traffic time intervals between arbitrary points on the road network. In addition to traffic density estimations that should be easily interpreted by users in a very short time. Moreover, demonstrating real-time routes for drivers based on the embedded navigation systems such as Global Positioning Systems (GPS). Another challenging requirement lies in storing huge amounts of traffic information generated from progressively complex networks of sensors, where Big data comes into play. However, collecting and storing this amount of traffic information is not enough. It is crucial to correlate based on game theory methodology, validate and make use of data in real-time. Hence providing valuable, relevant data extraction and insightful predictions of upcoming patterns and trends based on historical data. As an example, providing drivers with real-time traffic information to assist them in finding out the best road routes. This is why the subsequent role of predictive analytics for the whole transportation is needed. Consequently, traffic information systems require participation of all of the above components to interact and integrate through a common infrastructure. It allows immediate transmission of real time traffic information to any part of the system ( Abdelkader et al., 2021 ).
Even with the aforementioned benefits and challenges that come along with the integration of IoT into the transportation sector. IoT provides a paradigm shift that changes the transit services into intelligent groundbreaking systems, where numerous cutting edge technologies are incorporated. This creates a wide suite of intelligent transportation applications that have road users’ experience and safety at its core. A widely adopted IoT applications in the automotive industry include: the integration of sensors such as weight measurements and real-time fleet location sensors-based tracking to help fleet operators efficiently manage their fleets. Another use case that IoT technologies have shown great impact is predictive analytics. In this context, drivers are provided with early in advance vehicle maintenance alerts in cases of failure of a specific vehicle component. This is because these components are equipped with sensors that collect and share real-time information on the vehicle’s status with their vendors. It avoids any sudden or abrupt failure that can cause a life-hazard situation. Last, but not least with a precise focus on the integration of IoT with connected mobility. Figure 3 showcases a predictive maintenance scenario, where an in-vehicle monitoring system acquires IoT sensing data from the faulty in-vehicle sensor. The vehicular data is sent to the Diagnostics and Prognostics cloud services for analyzing and predicting maintenance issues. Repair recommendations are then sent back to the drivers ( Kshirsagar and Patil, 2021 ).
FIGURE 3 . An IoT-based predictive maintenance use-case scenario.
3.1.3 Emergency response
Crisis management is one of the critical situations that face many governments, first responders, emergency dispatchers and others who provide necessary first aid/assistance at the least possible time. In such situations, the design of the required infrastructure to handle emergencies becomes a critical requirement. With the introduction of IoT technologies to the safety systems where a suite of sensors are connected to provide real-time data to crisis management officials. This includes the use of sensors to monitor the water levels in cases of flood situations to provide insights that can support real-time data analytics to manage flooding crises. Real-time information can contribute to improving crisis management response time. Hence, reducing or eliminating the costs of crisis-related damages. Firefighting is considered a viable use case that finds value in IoT applications. Heat-proof sensors placed in indoor buildings can provide real-time information about the initial starting point of a fire, spreading patterns and intensity levels ( Mekni, 2022 ). It also extends to provide additional safety measures for firefighters as they use IoT-based safety alert devices that can accurately detect their motions. These devices are equipped with acoustic transmitters which act as beacons to allocate firefighters within the building and embedded sensors that can monitor their vital health conditions. Besides protecting firefighters, IoT-based sensors are employed to sustain indoors electrical systems and smartly pre-identify any active heat sources through abrupt temperature spikes. Immediate alerts are then subsequently sent for rapid and instant inspections. Fire systems based on IoT solutions can be actively intelligent to detect and promptly put off small fires through the use of smart sprinklers. Other emerging solutions that aid firefighters in fire crisis situations include computer-aided dispatch data such as precise fire locations, environmental conditions and others. Augmented reality-IoT based firefighter helmets ( Choi et al., 2021 ) are another innovative solution that can effectively guide firefighters to navigate in low-visibility conditions.
Emergency responders and dispatchers are leveraging the benefits of IoT-based solutions when dealing with daily traffic accidents. Numerous automotive industrial solutions such as GM OnStar provide a myriad of applications and services to assist first aid dispatchers. This could be achieved through utilizing cellular networks in conjunction with GPS and IoT technologies. Leveraging Vehicle to Infrastructure (V2I) communication-based technologies to provide critical information incases of traffic accidents. These include avoidance crash response, where drivers in crash situations can connect to OnStar call center by requesting the appropriate help to be provided to the vehicle’s location. The ecall can be activated manually (using a push-button) or automatically through data collected from on-board sensors. Other applications include stolen vehicle assistance which help the authorities in locating the stolen vehicle by activating several functionalities. This includes halting the restart option upon reactivation of the remote ignition block and transmitting a slowdown signal to let the vehicle come to a stop eventually ( Abboud et al., 2016 ). Other use cases include amber alert notifications sent to the public by integrating IoT and cellular network technologies. Amber alerts provide new means of aiding emergency responders and authority officials in risky situations such as child abduction. Officials collect crowdsourcing witness information from people within close event proximity to assist in their investigations. The alerts usually include event description (time and location). In addition to the Kidnappers’ detailed information (e.g, vehicle’s information, license plate number and their description) as well as child description. However, inability to correctly track the suspects’ vehicle or missed notifications by the public may contribute and lead to inefficient amber alert-based systems ( Zhang et al., 2018 ).
Traditionally, infrastructure failures and power outages are other use cases implying sudden and abrupt crisis situations that may be disruptive to emergency officials. Based on leveraging IoT technologies that aim at providing preventive and predictive maintenance. Hence, avoiding sudden breakdowns, anomalies and damages of the infrastructure. This could be achieved through continuous supervision and monitoring. For instance, smart bridges include a modular and IoT-sensor based system for monitoring, evaluating and recording any changes of the bridge structure in near real-time. Embedded sensors in the core structure of the bridge can then relay measurable information to management officials for further analysis. This includes humidity, temperature and corrosion status of the structure. Such data is considered valuable to constantly assess and evaluate the health structure and provide necessary measures such as intervention and predictive measures strategies ( Yang, 2003 ).
3.2 Home automation
Home automation and control systems are essential components of smart cities and have played a significant part in the advancement of our home environments. They have several applications for different usage at home, such as entertainment and smart living, surveillance, and safety management ( Alhafidh and Allen, 2016 ). Home automation is described as a standard home environment equipped with IoT technological infrastructure to provide a safe and comfortable lifestyle ( Khoa et al., 2020 ). Home automation is based on an intelligent, self-adaptive system that analyzes and evaluates stakeholder behaviors and has the capability to predict the stakeholder’s future actions and interact accordingly. Home automation systems use image detection and facial recognition models that are embedded in an intelligent control system connected to different sensors such as light sensors, motion sensors, water leak sensors, smoke sensors, and CCTV cameras ( Pavithra and Balakrishnan, 2015 ). These devices communicate with each other through a gateway that is distributed throughout a home area network. The home control system will connect different subsystems that cooperate in modeling the stakeholder’s actions and the environment’s information such as temperature, humidity, noise, visibility, and light intensity to enhance the learning process. For example, lights and AC temperature can be controlled and automated to adapt to the stakeholders’ needs and their movements in the home environment. This would conserve energy while also effectively monitoring energy consumption ( Vishwakarma et al., 2019 ). Research on home automation is not restricted to energy optimization; it involves health monitoring and security measures. By using innovative IoT technologies, we can connect to surveillance cameras in the home environment via a mobile device. Additionally, stakeholders can have access to doors and window sensors to maintain home safety and security remotely ( Alsuhaym et al., 2021 ).
3.3 Industrial sector
Industrial IoT leverages IoT capabilities in business and economic sectors to automate previously complex manual operations in order to satisfy consumer needs and reduce production costs. Warehouse operations, logistical services, supply chain management, and agricultural breeding can have machine-to-machine (M2M) intercommunication to ensure optimal industrial operations ( Pekar et al., 2020 ). Figure 4 illustrates a scenario of the IoT communication sensors in a smart agricultural system. This smart agriculture system monitors and analyzes the environmental parameters using soil moisture and harvesting sensors such as ZigBee, EnOcean, Z-wave and ANT ( Tang et al., 2018 ). These sensors are automated to diagnose the status of a plant and gather this data through an IoT platform to take the proper action such as when to irrigate in consultation with a weather forecasting service available in the Cloud; thus ensuring the efficient use of water resources.
FIGURE 4 . Illustration of a smart agriculture system.
3.4 Logistics and supply chain
Supply Chain Management (SCM) is a crucial service in our world. Since 1900 ( Lummus and Vokurka, 1999 ), humanity has evolved SCM to meet the market needs. Figure 5 highlights SCM milestones. Before 1900, SCM was restricted to the local areas. However, due to the revolution in railways, goods now can reach far beyond local borders. Between 1900 and 1950, global SCM attracted large players and organizations like UPS began providing their services in the SCM field. Industry leaders started to look for how we could improve the mechanization of the SCM process. From 1950 to 1970, the SCM community gained a superior experience by analyzing the military logistics of the First World War. DHL and FedEx were established as logistics enterprises, and IBM built the first computerized inventory management that was capable of handling complex inventory problems and making stock forecasts. In 1975, JCPenney designed the first Real-Time Warehouse Management System (WMS) that monitors the warehouse stock in real-time. Seven years later, Keith Oliver introduced the Supply Chain Management term. In the 90s, the technological revolution was triggered when many enterprises deployed computers to manage their processes and the internet to reach their customers through the World Wide Web. In the 90s, Amazon started running the e-commerce website. The 4.0 industrial revolution, including the internet of things, began growing in the last decade. Although the IoT looks like a promising technology to be adopted in the SCM field, deploying IoT in SCM faces many challenges. The main hindrance ( Haddud et al., 2017 ) is the integration of different supply chain processes due to The heterogeneity of technologies used in various supply chain stages.
FIGURE 5 . Supply chain milestones.
COVID-19 ( de Vass et al., 2021 ) uncovered a new factor that magnifies the importance of relying on information and communications technology to run the SCM systems. Businesses had to switch to remote working due to the pandemic. The lockdown and physical distancing requirements imposed on suppliers reduced their labors in their plants and sometimes obliged to shut down to limit the virus spreading. As a result, consumers face product shortages due to reduced production volumes during the pandemic. To date ( Ozdemir et al., 2022 ), the world is still suffering from the devastating effects of COVID-19 on the supply chain. Therefore, decision-makers ( Baldwin and Di Mauro, 2020 ; Baldwin and Tomiura, 2020 ) started exploring how we could deploy new technologies, such as IoT, for managing remote operations.
IoT sensors and devices shifted the landscape of portable and wearable medical devices from fitness and wellness devices to medical-grade devices qualified for usage at hospitals and healthcare providers. This shift accelerated the integration of remote patient monitoring in hospitals to accommodate patients with chronic diseases ( Casale et al., 2021 ). Therefore, numerous efforts have been conducted to advance remote patient monitoring (RPM) systems with the help of well-established IoT infrastructures and standards in the healthcare domain ( El-Rashidy et al., 2021 ). The RPM systems are expected to match or exceed the performance of the existing monitoring and examinations administered at hospitals and healthcare facilities ( Casale et al., 2021 ). For example, continuous heart rate monitoring and immediate heartbeat detection necessitate patients to be hospitalized and/or connected to a Holter monitor or similar devices for long-term cardiac diagnosis. However, this setup would hinder patient mobility due to the limitations of the existing devices in terms of size and the number of attached wires. Moreover, hospitals dedicate significant resources to providing long-term cardiac monitoring that, in some cases, is unavailable, especially in low or middle-income countries. Therefore, RPM systems effectively reduce death from chronic diseases (e.g., heart diseases, diabetes). IoT platforms and devices significantly accelerated the development and integration of RPM systems into existing healthcare infrastructures. To that extent, a typical RPM implementation constitutes various services but is not limited to data acquisition, tracking, communication, automated analysis, diagnoses, and notification systems ( Miller et al., 2021 ).
4 Rising trends in sensor data analytics
In recent years, the IoT domain has witnessed increasing interest by the research community and rising demands from the industrial sector to embed real-time data analytics tools into the core of IoT standards. While the real value proposition of IoT is shifting from providing passive data monitoring and acquisition services to autonomous IoT applications with real-time decision-making services. Consequently, real-time data analytics is no longer an add-on service and has become integral to any IoT application rollout. For example, remote patient monitoring (RPM) and real-time data analytics have significantly contributed to enhancing ECG monitoring and enabling healthcare providers to gain 24/7 access to their patients remotely, especially for patients with coronary diseases ( Mohammed et al., 2019 ). However, sensor data acquisition and collections are mapped as the foundation of IoT applications yet are considered passive techniques due to the absence of intelligence or decision-making. The main goals of IoT application at the early stages were to collect and monitor significant information regarding specific applications as initially proposed in 1999 ( Butzin et al., 2016 ) while developing supply chain optimization at Procter & Gamble. Nearly after 2 decades, the goals of using IoT applications and their expectations are on the rise, demanding proactive and active decisions made on sensor data collected in real-time. Accordingly, data analytics permits various applications to focus on performing real-time diagnoses, predictive maintenance, automated decision-making, and theoretically improving the productivity and efficiency of the intended applications. Meanwhile, modern stream processing engines (e.g., Apache Kafka and Apache Pulsar) come with built-in APIs ready for data analytics integrations ( Martín et al., 2022 ). Moreover, most cloud services provide ready-made end-to-end event processing and real-time data analytics tools (i.e., Google DataFlow).
4.1 Real-time vs. offline data analytics: Differences, needs, and potential use
In an IoT-driven society, applications and services integrate smart learning approaches for analyzing insightful patterns and trends that result in improved decision-making. For more effectively optimized analytics, several IoT data-specification characteristics should be considered. These include dealing with huge volumes of data streamed from sensor-based devices deployed for IoT applications and services. It requires new means of big data analytics that can deal with huge volumes of sensor-generated data. In this context, conventional hardware/software methods for storage, data analytics and management purposes cannot handle such huge volumes of streaming data. Moreover, information collected from heterogeneous devices result in three significant common features among IoT Data. These features include data heterogeneity and association of time/space stamps based on the sensors’ locations. The third feature is the subjectivity of IoT data associated with the high noise levels during acquisition and transmission processes.
Beyond such characteristics that utilize big data analytics approaches, a new suite of applications and services arise that demand prompt actions in real-time analytics. This is primarily due to its time sensitive and fast streaming of IoT data that is generated within short time intervals for instant decision making and actions. These insightful decisions are time stringent, where IoT streaming data analytics need to be delivered within a range of hundreds of milliseconds to only a few seconds. As such, life-saving applications demand fast and continuous streams of incoming data associated in some cases with real-time multi-modal data sources for efficient decision making. For instance, connected and autonomous vehicles require data fusion of real-time sensor data from different sources (e.g., Lidars and cameras), V2X communication and road entities (e.g., traffic lights) for safe perception decision making. Transmitting traffic data to the cloud servers for real-time analytics will be liable to network and communication latency that are not well-suited for time sensitive applications, which may result in fatal traffic accidents. However, analyzing real-time streaming data on powerful cloud computing platforms that adopt data parallelism and incremental processing techniques can reduce the end-to-end delay associated with two-way data transmissions. A more optimized approach could reside in solutions such as edge computing, where data analytics are closer to the data sources (e.g., edge or IoT-based devices) for faster data analytics ( Goudarzi et al., 2021 ). However, these solutions are still prone to a number of limitations including limited computation, power and storage resources on IoT devices. The rising trends towards real-time data analytics are also striving in non-critical business applications.
4.2 Decision making
Leading IoT-based business sectors rely heavily on well-analyzed real-time data inferred from their IoT-enabled products. For critical and unbiased decision making, real-time data analysis by machine learning algorithms can assist in eliminating/reducing junk information and estimating learning useful patterns. Data-driven analytics will provide more in depth insights for optimizing customers’ experiences through daily behaviors and patterns analysis. As an example, Apple watches can monitor our daily exercises and sleeping patterns in real-time and assist in providing customized preference notifications. Uber can also make informed decisions based on analyzing real-time demands for traffic trips. This determines their pricing rates that proportionately increase in rush hours. Other examples may include placing sensors within oil tanks for real-time monitoring of oil fluid levels, temperature and humidity. This initiates automated decision making such as oil reordering and planning pre-scheduled maintenance ( Moh’d Ali et al., 2020 ).
Decision making-based systems can be classified according to the different levels of system complexity. This includes visual analytics systems that help business practitioners to analyze and interpret gathered IoT data. Business intelligence embedded dashboards aid in presenting the retrieved IoT information in a meaningful manner. Automated and warnings-based systems conduct a predefined data analysis that assists in highlighting risky situations through alerts and warnings. For example, IoT-based real-time environmental monitoring systems can track pollutants and chemicals’ levels in the air within an industrial city. Warning notifications are then subsequently sent to citizens within the affected geofenced area indicating health risk hazards. Reactive-based systems may take a step forward towards performing actions described through rule-based languages that are carried out when specific conditions are met. For instance, smart lighting IoT-based systems may switch off the lights in a specific building area if no one is present, which is indicated by infrared occupancy sensors ( Wang et al., 2017 ).
4.3 Predictive maintenance
Utilizing IoT applications has incredibly reduced maintenance costs, in particular in the industrial sector. For example, industrial equipment manufacturing that embed sensors in heavy machinery integrate with analytical tools to monitor the operational efficacy, detect faults or failures, and provide a full assessment of the operating condition ( Mobley, 2002 ). This comprehensive performance evaluation occurs on a regular basis to maintain the system’s efficiency and initiates maintenance if needed. This procedure is known as Predictive Maintenance (PdM), or condition-based maintenance, and it employs diagnostics and prognostics data to spot early signs of failure, allowing the system to operate as intended ( Zonta et al., 2020 ). Furthermore, PdM can estimate degradation of the equipment and predict the remaining useful life (RUL) of equipment, which reduces the maintenance costs to the minimum and assures service availability. According to Selcuk ( Selcuk, 2017 ), IoT-based predictive maintenance increases the return on investment by 10 times, where this approach increases the total production by 15%–70% and reduces the maintenance costs by 25%–30%. Although PdM successfully reduces the cost of production and maintenance, it is expensive to implement due to the high cost of the hardware and software required to effectively incorporate the PdM into the system. Moreover, the quality of the training services and the amount of data required to ensure the efficacy of the PdM performance can be challenging ( Compare et al., 2019 ).
4.4 Operation optimization and automation
Industry 4.0 is transitioning from a concept-based approach into a market reality. Through the integration of intelligent and computerized robotic devices into many aspects of industry verticals (e.g., 3D printing, E-sports) that can assist in automating and optimizing the manufacturing operations. This allows accurate, timely and cost effective completed manufacturing processes among a set of machines with minimal or no human interventions. In addition to decrease in cost-related operations through effective inventory management and energy consumption optimization. Effective inventory management in logistics and supply chain sectors is obtained through the integration of IoT with Radio Frequency Identification (RFID) ( Tan and Sidhu, 2022 ) and barcode scanners.
Furthermore, IoT technologies within business automation can efficiently be used for controlling and monitoring machines’ manufacturing operations, performance and rate of productivity through internet connectivity. Moreover, real-time analysis generated from onsite IoT-based sensors provides valuable insights to initiate more efficient ways for decreasing cost-related operational expenses and safety-related/unplanned maintenance issues. For example, incases of machine operational failures, an IoT-based system can promptly send a machine repair request to the maintenance department for handling the issue. Furthermore, with the introduction of IoT technologies, business revenues are subsequently expected to increase due to the incremental rise in operational productivity. This can be achieved through analyzing three critical aspects including operational data, timing and the reasons for any production issues. This allows business leaders to be focused on their high-level core business objectives with a well-defined automated workflow.
4.5 Enhanced customer experience
Connected environments enable businesses to adopt a user-centric approach which utilizes IoT technologies for enhanced overall customer experience and extend the customers’ loyalty towards their services and products. IoT-driven businesses are the gateway to futuristic enhanced digital customer experience and prolonged loyalty which are considered one of the primary laser-focus objectives by many brands. The drive towards more personalized services and applications by customers urge many enterprises to increase services for customer engagement through the use of artificial intelligence-based customer support systems for real-time assistance.
The aforementioned notion of providing level up services that incorporate personalized experiences initiated many innovative applications and services. As such, omnichannel customers’ applications and products such as smart-based home appliances and devices including Alexa-supported devices, Nest Thermostat and intelligent Ring Doorbell cameras that enable customers to use voice assisted technologies along with IoT to control various aspects of their home intelligently. Moreover, ubiquitous smart wearable devices such as fitness trackers that collect real-time health data related to customer behavior and daily routines to enhance customer experience. For instance, providing customers with real-time personalized notifications according to their daily activities. Figure 6 demonstrates the users-centric experience among various IoT services and applications.
FIGURE 6 . User-centric IoT application scenarios.
However, privacy and security data leakage are still considered a major challenge that many researchers and developers are trying to find innovative and tangible solutions to secure personal information when shared for improved service and application experiences.
4.6 Asset tracking and monitoring
Introducing artificial intelligence (AI) into IoT applications has created significant opportunities for innovations in automation and asset tracking domains. Companies and labour-intensive corporations are investing in autonomous working environments with less human interaction, and the demand for AI and context-aware systems has drastically increased. Whereas in times similar to the coronavirus pandemic, factories and workplaces have entirely shut down because of lockdown measures to prevent human interactions. The fusion between AI and IoT transitioned traditional industry models to the industry 5.0 revolution. AI and IoT as core technologies to industry 5.0 along with wireless sensor networks result in more benefits to industries like using analytical techniques to provide predictive maintenance notifications directly affecting downtime, improving workforce and increasing production efficiency. IoT sensors and devices can perceive and sense their environment through high-level technologies, such as laser scanners, cameras and image processing, movement and proximity. Therefore, real-time decisions are made autonomously regarding object identification and asset tracking by coupling these technologies (i.e., image-recognition software). Similarly, IoT applications based on AI algorithms can learn and think logically about different operations that require problem-solving schemes. Autonomous applications operate based on the receding-strategy approach, where new and old control inputs are carried out simultaneously through computing the new control inputs and executing the old ones. The application creates these control inputs to provide real-time performance based on three hierarchical levels. The higher level is concerned with defining complex operations, for example, GPS waypoints for an autonomous device (e.g., robot) to follow. The other two (mid and lower) levels are precisely related to creating and tracking a reference trajectory for this course, respectively. The safety of the robot is the responsibility of the mid-level controller ( Vaskov et al., 2019 ). More concisely, safety concerning collision avoidance among a group of automated robots performing a specific task can be avoided by sharing their perceived data. Other communications that involve Human-Robotic communication are based on the models, such as imitative learning and artificial neural networks.
5 Open challenges
5.1 device and data heterogeneity.
The versatility of IoT devices and sensor nodes in various fields has given rise to many applications. While deep learning, AI, and many other enabling technologies assist IoT devices in learning by experience and adapting to new environmental inputs to be able to conduct complex operations. However, the reliance on receiving data representing the context of the environment surroundings specific to the IoT application requires a significant number of different sensors and devices. Individual sensors or applications provide limited cognition and visibility of the surrounding environments. Therefore, integrating various sensors is essential in context-aware applications. Furthermore, the diversification of sensor nodes and devices raised numerous challenges in the research community and the industry with respect to unification and standardization. In public sensing, different types of sensors are used (e.g., RFID, Ultrasonic, Cameras, Lidars, etc. ) to solve designated issues like real-time counting of people waiting to be served at a specific service provider. The same extends to IoT applications similar to the public sensing domain like traffic management and predictions. Therefore, the demand for modular platforms with unified application programming interfaces (APIs), transmission protocols, data transformation and storage is growing. Moreover, data conversion and normalization operations carried out in applications with heterogeneous devices increase exponentially due to the diversified number of sensors utilized just for one application (e.g., autonomous vehicles).
Behmann ( Behmann and Wu, 2015 ) described current IoT solutions as point solutions where they are isolated and cannot interact with each other. Collaborative IoT (C-IoT) ( Behmann and Wu, 2015 ) is a recent trend that is still unsaturated and needs more effort to be deployed in real-world scenarios. Sharing the infrastructure and data becomes inevitable to pave the way for C-IoT systems. C-IoT can create an expandable ecosystem, and the IoT community will solve complicated problems by relying on the collaboration between IoT systems. For instance, an ambulance in the emergency state can always have a green light on its way if the emergency service can share the ambulance’s route with the city’s intelligent traffic system using the shared infrastructure of C-IoT. Recently, an active movement to have a unified standard in different IoT layers has been raised for a few years to mitigate incompatibility challenges faced by the C-IoT trend.
The diversity of IoT devices in the perception layer brings the flexibility to build customized IoT solutions and cherrypick the appropriate device for a specific task that matches constraints regarding the accuracy, cost, compatibility with the existing infrastructure, etc. However, this also comes at the cost of the absence of a unified ecosystem for IoT. This results in interoperability issues between different IoT systems and increased development time to get diverse IoT devices to act as a coherent system. Besides, it leads to the notorious “isolated islands” of miniature IoT subsystems based on the brand of devices or their enabling technologies, hindering the utilization of sensed information by different systems to its maximum and impeding the potential promise of IoT.
5.3 User and data privacy
The constituent IoT gadgets of an IoT system typically consist of consumer electronics (e.g., smart TVs) and wearable devices (e.g., smart watches) that gather a lot of information about people, which was previously hard to collect. Gathered data by IoT devices may include personal information of the users (e.g., name, birthdate, etc. ), their biometric information (e.g., fingerprint, voice recognition, etc. ), and their preferences (e.g., eating habits, preferred movie genres, etc. ), which are usually part of the device’s initial setup, registration to its cloud platform, or for the device to be able to perform its smart designated task efficiently. Moreover, advanced IoT systems typically involve the aggregation of numerous pieces of information from heterogeneous smart objects, which is known as “sensor fusion” ( Abdelmoneem et al., 2018 ) to provide accurate and comprehensive data about the environment, including people themselves, to help make better informed decisions. Thanks to the advancements in artificial intelligence (AI) domain technologies which can leverage granular data collected by smart objects to generate inferences that would not be achievable with coarser data from individual smart objects. This intelligence imparted to IoT catalyzes its wide adaption and makes it quite useful in different application domains. However, user privacy concerns are still an open challenge to IoT that impedes its widespread adoption and limits its potential promise. IoT systems can disclose identifiable information about people without their consent. Therefore, amidst the potential promise of IoT to change the way we deal with our surroundings, users are mostly worried about the potential of private information leakage ( Chanal and Kakkasageri, 2020 ). They are worried about who owns their data and how it is utilized. Nonetheless, the notorious correlation between service providers and device vendors on one hand, and data brokers on the other hand, raises concerns about the possibility of their personal information being disclosed for non-public interest objectives. People frequently alter their behavior when they suspect that their identifiable information and activity footprints are being monitored, which reduces their freedom, changes their lifestyle, and makes them sceptical of IoT. In the following subsections, we review data privacy concerns that are associated with IoT.
5.3.1 De-identification of IoT data
Generally, it is prohibited to make datasets that include identifiable personal information publicly accessible. One common way to prevent exposing personal information in datasets is to avoid gathering information that could be used to identify people in the first place and whenever possible. For example, PIR sensors could be used for occupancy detection rather than surveillance cameras. However, given the penetration of IoT in a lot of domains with differentiated requirements, it is usually hard, or even impossible, to preclude the inclusion of identifiable information in gathered datasets by IoT. In this context, de-identification ( Kim and Park, 2022 ) is the process used to anonymize identifiable personal information from datasets, which is quite challenging. Hashing algorithms are commonly used to pseudonymize identifiable information in datasets by replacing identified people in a dataset with their unique hash token. However, since different datasets often have a lot in common, it is usually easy to figure out who the hashed information belongs to using inference techniques.
Consent ( O’Connor et al., 2017 ) is the typical justification for businesses to collect, use, and disclose personal information. However, consent often matters more than just unconsciously clicking the “I agree” button by the end user on the “Terms and Conditions” statement page of a device. Rather, consent that is meaningful and effective requires well-defined and finely-grained structured objectives that the user should be able to choose from. Moreover, one can not presume that consent will last forever. Therefore, consent methods should represent a single acceptance at a single moment in time, which may not be suitable for the continual nature of IoT. Also, given the interoperability nature of the IoT, where a smart sensor node may be utilised by different systems with different privacy policies, an individual can not grant meaningful permission for the use of their personal data for vague or broad purposes.
5.4 Vendor lock-in
IoT vendors and service providers typically maintain the security of their active devices or services by regularly providing firmware patches and system updates that address security vulnerability issues that continually emerge. However, they often have different expectations about how long their products or services will last than the people who buy them. For instance, vendors may terminate technical support or firmware maintainability of a device, or the service provider may discontinue the service that the device relies on to operate, far before the end user plans to retire the device. This usually comes at the cost of possible security holes, privacy issues, and vendor lock-in ( Fantacci et al., 2014 ). Therefore, customers would have to stick with their active line of products and services to keep their systems safe and operational because suppliers would no longer care about security and privacy issues with their retired devices or have the skills to deal with them.
5.5 Device management
The “plug and play” feature that usually accompanies IoT devices makes them user-friendly since customers can seamlessly get them up and running effortlessly without the need for complicated setup procedures. However, this sometimes comes at the cost of potential user privacy exposure since the default setup of devices usually comes with insufficient privacy and security precautions. Nonetheless, the fact that a gadget is an IoT device that can collect personal information and send it to third parties over the cloud may not be even realized by the majority of non-technically savvy customers by default. In addition, most IoT customers find it hard and time-consuming to adjust the privacy settings for each device in the system. This is because IoT does not have a standard ecosystem and is often made up of many devices from different manufacturers, each with their own user experience interface.
The extensive and distributed nature of IoT systems, which typically include different service providers that handle the collected data to achieve the designated task of the system, makes it challenging to precisely determine who is responsible for what. In order to achieve a robust and highly reliable IoT system architecture, system designers usually follow a common system architectural model that is known as “microservices” ( Butzin et al., 2016 ). They divide the ultimate task of the system into small independent tasks, each of which may utilize numerous services from different service providers that communicate over well-defined APIs. However, this raises privacy concerns because the collected data, which may contain personally identifiable information about users, is now handled and commonly stored by various third-party organizations with hazy boundaries that may apply different privacy policies.
5.8 Security (data and device vulnerability)
IoT devices exchange data with millions of devices through the internet which implicitly exposes the IoT devices to the vulnerabilities and security threats of the Internet protocol stack ( Ilyas et al., 2020 ). The amount of data collected, stored and shared between IoT devices and the service providers are expected to grow significantly. Besides the extraordinary amount of data produced by IoT devices, they induce evidently high-security risks and potential cyberattacks destabilizing many applications and industries.
IoT networks come with their unique security challenges ( Khanam et al., 2020 ), where each layer is exposed to certain types of attacks ( Hassija et al., 2019 ), like Distributed Denial of Service attacks (DDoS) on the network layer. To that extent, a multitude of surveys citerefs2 and studies have been conducted to expose existing security threats and vulnerabilities in current IoT applications. Recent surveys on IoT data and device security emphasize that the gap between applying existing security techniques to emerging IoT applications is growing significantly. The security gaps in IoT applications are categorized into vendor-related security issues and available resources or capabilities on the IoT nodes. IoT vendors for sensors and devices are moving towards low-cost manufacturing that lack security features. Similarly, the heterogeneity of the IoT applications, protocols, and hardware increases the security scope of threats in IoT applications ( Hassija et al., 2019 ). On the other hand, IoT applications are inherently constrained by the limited processing and storage capabilities of devices to carry out sophisticated security techniques. Therefore, new security measures are introduced for IoT resource-constrained devices using robust ML techniques like the TinyML framework. The concepts introduced behind the integration of ML is to increase the flexibility of IoT nodes in defending against emerging security threats ( Dutta and Kant, 2021 ). IoT devices can then train the deployed ML models to work against new security threats.
5.9 Open deployments and access control
Mentioning the access control usually flashed RFID (Radio-frequency identification) cards. RFID technology sparked the existence of the IoT term coined by Kevin Ashton, who considered RFID a vital component in The IoT systems. Access control as an open challenge is a multifaceted challenge that has been raised due to other issues. In this section, we use a wallet holding many RFID cards as an example to discuss the access control issues:
• Heterogeneity: Having many cards to access different purposes itself is due to the heterogeneity of the systems. There are chances to have a consensus among some corporations to unify their access cards to mitigate this challenge. For instance, Google Pay is an android application that offers contactless purchases on the smartphone with a built-in Near Field Communication NFC module. Users can register debit or credit cards and use them in their daily in-person shopping instead of holding many bank cards in wallets. Due to systems heterogeneity, we still face a challenge in making all services accessible from unified access.
• Security: Giving accessibility to banks a counting using contactless RFID bank cards looks a very smooth user experience in transactions instead of writing sixteen digits in card readers. However, card skimming devices can clone contactless bank cards.
To discuss the other issues in access control, we use a smart home application as another example facing access control challenges:
• Interoperability: Recently, users can simply control their IoT devices in their houses, such as smart TV, fridge, coffee machine, and adapted light systems. The interoperability between these devices is still very challenging due to the lack of standardization. For instance, a coffee machine starts pouring coffee on a cup if the adapted light senses a motion in the living room, and there is a collaborative integration between these devices. ThingsDriver ( Elewah et al., 2022 ) is a beginning to have A Unified Interoperable messaging protocol that, if adopted by cooperates, can pave the way to have a collaborative environment.
• Privacy: All these smart home devices become remotely accessible through user-friendly user interfaces such as a smartwatch or phone. On the other side, the flexibility of accessibility raises privacy concerns. Residents’ data are highly vulnerable to being breached by unauthorized access.
In this paper, we review the Internet of Things technology from different architectural, technological, operational and value-proposition perspectives. We first shed the light on the definition, acclaimed value and potential, and unique features and characteristics compared to similar previous technologies and standard layered architecture. We then highlight the different applications of IoT in various life domains that primarily benefit from its realization as a novel computing paradigm. We outlined the grand challenges facing IoT which may cause slowdown in its widespread adoption at the individual, organizational and governmental levels.
We believe the IoT will continue to grow as a disruptive technology that changed the world and it will never be the same again. There is a continuously increasing reliance on IoT technology in different sectors of our life for its convenience and innovative applications stemming out of it. Individuals and enterprises started to gain confidence in the technology and overlook or ignore the downsides of its security and privacy aspects. However, we also believe that the emergence of Edge computing in its different forms and shapes has contributed to lower the adoption barriers of IoT and increased interest in its technology and smart services. We anticipate that in the next few years IoT will continue to penetrate deeper in various sectors and tape into more industrial and governmental settings.
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: internet of things, IoT challenges, IoT new trends, IoT applications, IoT grand vision
Citation: Elgazzar K, Khalil H, Alghamdi T, Badr A, Abdelkader G, Elewah A and Buyya R (2022) Revisiting the internet of things: New trends, opportunities and grand challenges. Front. Internet. Things 1:1073780. doi: 10.3389/friot.2022.1073780
Received: 18 October 2022; Accepted: 07 November 2022; Published: 21 November 2022.
Edited and reviewed by:
Copyright © 2022 Elgazzar, Khalil, Alghamdi, Badr, Abdelkader, Elewah and Buyya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Khalid Elgazzar, [email protected]
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Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future
a LTEF-Laboratory for Thermodynamics and Energy Efficiency, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Rudjera Boskovica 32, 21000, Split, Croatia
b Department of Electronics, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Rudjera Boskovica 32, 21000, Split, Croatia
Diego López-de-Ipiña González-de-Artaza
c Faculty of Engineering, DeustoTech - Fundación Deusto, Universidad de Deusto, Despacho 545 Avda, Universidades 24, 48007, Bilbao, Spain
d Department of Innovation Engineering, University of Salento, Ecotekne Campus - S.P. 6, Lecce, Monteroni, 73100, LECCE, LE, Italy
The rapid development and implementation of smart and IoT (Internet of Things) based technologies have allowed for various possibilities in technological advancements for different aspects of life. The main goal of IoT technologies is to simplify processes in different fields, to ensure a better efficiency of systems (technologies or specific processes) and finally to improve life quality. Sustainability has become a key issue for population where the dynamic development of IoT technologies is bringing different useful benefits, but this fast development must be carefully monitored and evaluated from an environmental point of view to limit the presence of harmful impacts and ensure the smart utilization of limited global resources. Significant research efforts are needed in the previous sense to carefully investigate the pros and cons of IoT technologies. This review editorial is partially directed on the research contributions presented at the 4th International Conference on Smart and Sustainable Technologies held in Split and Bol, Croatia, in 2019 (SpliTech 2019) as well as on recent findings from literature. The SpliTech2019 conference was a valuable event that successfully linked different engineering professions, industrial experts and finally researchers from academia. The focus of the conference was directed towards key conference tracks such as Smart City, Energy/Environment, e-Health and Engineering Modelling. The research presented and discussed at the SpliTech2019 conference helped to understand the complex and intertwined effects of IoT technologies on societies and their potential effects on sustainability in general. Various application areas of IoT technologies were discussed as well as the progress made. Four main topical areas were discussed in the herein editorial, i.e. latest advancements in the further fields: (i) IoT technologies in Sustainable Energy and Environment, (ii) IoT enabled Smart City, (iii) E-health – Ambient assisted living systems (iv) IoT technologies in Transportation and Low Carbon Products. The main outcomes of the review introductory article contributed to the better understanding of current technological progress in IoT application areas as well as the environmental implications linked with the increased application of IoT products.
With rising technological developments in society, new possibilities have occurred and that could simplify our daily life and provide more efficient services or production processes. Digitalization has allowed ‘‘smart’’ ( Zheng et al., 2019 ) to become the epicentre of already ongoing technological developments. In fact, IoT technologies are nowadays assumed to be one of the key pillars of the fourth industrial revolution due to significant potential in innovations and useful benefits for the population. On the other side, each development utilizes limited resources leaving behind different environmental footprints, ( Li et al., 2020a ), especially different kinds of pollutants, ( Zeinalnezhad et al., 2020 ). Internet of things (IoT) based technologies bring a completely new perspective on the further progress of various fields, such as for instance in engineering, ( Zaidan and Zaidan, 2020 ), agriculture ( Farooq et al., 2020 ), or medicine ( Salagare and Prasad, 2020 ), and in other fields that have not been explored yet. Some potential application areas in IoT technologies are still unknown or insufficiently clear on how to approach them which is an evident indication that more intense research activity should be conducted in this challenging field towards new and important potential benefits for society. Therefore, the relevance and importance of IoT technologies in future terms are more than clear and should play an important role.
The rise of IoT technologies is currently intense and according to projections for the next 10 years, over 125 ·10 9 IoT devices are expected to be connected, ( Techradar, 2019 ). The expected investments in IoT technologies are also high with expectations being over 120 ·10 9 USD by 2021, with a compound annual growth rate of about 7.3%, ( Forbes, 2018 ). The general present market structure of IoT technologies is presented in Fig. 1 , where it is evident that the majority of the market is focused on smart cities and industrial IoT.
General market structure of IoT technologies ( Nižetić et al., 2019 ).
If recent projects in IoT technologies are being analysed than most of them are in the field of smart cities and industrial IoT. Other significant potentials are connected buildings, connected cars and energy segments ( Forbes, 2018 ), but lower than the first mentioned fields. It is also found that the most rising trend in the number of IoT projects currently is as expected in smart cities, connected health and smart supply chain segments, with an annual rise over 30% in the EU and USA. Industrial IoT, connected cars and agriculture segments has recorded a decrease in the number of realized projects, i.e. over 25% in the USA and EU, ( Forbes, 2018 ). From a perspective of high upcoming population pressure on cities and because a population of almost 11 ·10 9 is expected by the end of the century ( Pewresearch, 2019 ), the smart city concept could become a vital one to allow for a normal operation of highly populated cities.
In order to support the rapid technical development of IoT technologies, as well as novel potential applications areas, specific technical issues would need to be resolved, ( Techradar, 2019 ). One of the main issues is associated with the development of different tools for the monitoring of network operations ( Kakkavas et al., 2020 ), then issues with security tools and their management, ( Conti et al., 2020 ), issues with software bugs, demanding maintenance of IoT networks, and finally security issues related to IoT networks, ( Almusaylim et al., 2020 ). The important problem linked with the efficient implementation of IoT technologies is linked with the available speed and coverage of wireless networks (Wi-Fi), where expectations are high due to noticeable increases in Wi-Fi network coverage in the period of 2017–2022 as well as increases in Wi-Fi speed Fig. 2 . In a global sense, increases in Wi-Fi speed are expected for more than a factor of two, i.e. from about 24 Mbps to more than 54 Mbps. The most intense increase in Wi-Fi speed is expected in the Asian region, ( Zdnet, 2018 ).
Expected increase in global Wi-Fi speeds in period of 2017–2022 ( Zdnet, 2018 ),
The lowest Wi-Fi speed is noticeable in the Latin America and Middle East&Africa regions, which are an indication of potential problems for the efficient implementation of IoT products or novel more advanced upcoming technologies.
An increased implementation of IoT technologies would lead to a more intense utilization of fossil technologies to ensure the necessary energy supply for IoT production lines. The production of electronic equipment is causing potentially unbalanced waste of limited metals and resources in general, which could become a critical issue in the long run. Unfortunately, the recycling rate of electronic waste is low and currently in the amount of about 20% ( Thebalancesmb, 2020 ) which makes matters questionable regarding the available resource capacity to produce IoT products when taking into accounts the rising market demands. The production of electronic gadgets has led to the consumption of various chemicals, water and finally fossil fuels that have all left environmental impacts. As already tackled, different metals are also being used to produce electronics such as copper, silver, gold, palladium etc. One of the major issues is the led content in e-waste and its severe impact to the environment. Recycling in the previous sense is very important, where the present recycling rate of electronic equipment is certainly not sufficient and must be increased. Globally, the annual rise of the recycling rate ranges from about 4% to 5% ( Thebalancesmb, 2020 ). The legislation related to the e-waste is one of the main drawbacks since more than 50% of world population is still not well covered with proper legislation related to e-waste, ( Globalewaste, 2017 ), which is preventing the further development of e-waste facilities. The market value of raw materials from e-waste is estimated to be more than 50·10 9 Euros, ( Globalewaste, 2017 ). Certainly, more strategic and targeted actions are needed in the e-waste issue to secure a more balanced and sustainable development of IoT technologies. Overall, the annual generation of e-waste is more than 44·10 9 metric tonnes, which is equivalent to more than 6 kg per inhabitant annually, ( Globalewaste, 2017 ). A potential exists and must be better utilized to ensure a sufficient quantity of valuable raw resources.
It should be highlighted that there is no doubt in what IoT technologies would bring to the table, such as various useful benefits to society and an overall improvement in life quality. Each technology has specific issues and drawbacks that need to be detected and closely investigated on time, since IoT technologies have the potential to change our lives and habits. Several important facts need to be emphasized when addressing IoT technologies to be able to understand the long-term effects associated with the fast development of IoT:
- - IoT technologies have caused an increase in the utilization of limited resources or raw materials where some of them have become rare or are already rare (for instance, specific precious metals for electronics),
- - The prices of electronic devices have become more acceptable, which means an increase in production volume, finally more resources are being utilized. A rebound effect is possible in that sense,
- - The long term environmental impacts of IoT technologies are unknown. A noticeable amount of energy would be needed to support the production and operation of IoT devices,
- - An increase in electronic waste is expected due to the large estimated number of IoT based devices in the near future,
- - In some sectors, IoT technologies could have social impacts due to the reduced necessity for labour and limitation of direct social contacts, which is vital and an important aspect for each human being.
The main point of the above raised issues is not to indicate and create a negative attitude towards IoT technologies but to carefully analyze the overall aspects in order to secure a smart and sustainable development of IoT technologies, which are a valuable opportunity for humanity.
1.1. Necessity for smart technologies
The world is rapidly changing, i.e. developing in a technological sense and is being driven by the present economic system globally. Unfortunately, each technological development has got its price, which can be sensed through the intense utilization of limited fossil-based resources and the production of various impacts to the environment, ( Chen et al., 2020a ). The population is constantly growing with an annual rate of about 1.1% per year with the current population being over 7.7·10 9 ( Data.worldbank, 2020 ). As previously addressed, the population concentration is in cities and according to UN projections, about 68% of the population will be living in cities by 2050, ( UN, 2018 ). A significant infrastructure pressure is expected in cities due to boosted urbanization, thus novel technological solutions would be key to secure the normal operation of cities in the given complex and demanding circumstances. In the previous sense, the general application of IoT and smart technologies would have an important role and could help to bridge some major infrastructure related issues in cities. The necessity for IoT technologies is closely linked with ongoing technological advancements and digitalization where a variety of different electronic products need to be somehow connected in a useful manner. There is a necessity for more efficient services and flexible processes in general, which could be obtained with the proper implementation of IoT technologies. IoT technologies have allowed for a variety of efficient services and smart networking, applications or devices that can ensure useful synergic effects and produce benefits. The major advantage of IoT technologies is their connectivity aspect that has enormous potential, Fig. 3 .
General structure of IoT network and connectivity ( Zhang et al., 2018 ).
Various benefits are possible and would be gradually integrated in our lives thorough upcoming years in different application areas and will be briefly discussed in the upcoming section of the introductory review editorial.
1.2. Application areas
The application areas of IoT are various and based on current available technological solutions, the most represented application sectors are shown in Fig. 4 . The most important and most progressing application areas of IoT are related to the industry ( Osterrieder et al., 2020 ) and smart city concept ( Sivanageswara Rao et al., 2020 ), with respect to the number of realized projects.
Application areas of IoT technologies.
The transportation ( Porru et al., 2020 ), smart energy management in buildings ( Douglas et al., 2020 ) or management of power networks ( Martín-Lopo et al., 2020 ), as well as the agriculture sector ( Villa-Henriksen et al., 2020 ) are also promising, having significant potential.
The development of specific IoT application areas strongly depends from several key factors such as:
- - general available advancements in electronic components,
- - available software solutions and user friendly surrounding,
- - solutions related to sensor technologies and data acquisition,
- - quality of network, i.e. network connectivity and infrastructure,
- - sufficient energy supply for production and operation of IoT devices.
In the continuation of the review editorial, some key IoT application areas will be briefly addressed together with the main developments and current challenges.
1.2.1. IoT in industry
The application of IoT technologies in industrial applications would allow for an increase in efficiency regarding the production process and would ensure more efficient communication and networking between operators and machines, Fig. 5 . Finally, it would allow for more competitive companies on the market with more efficient quality control with a minimization in losses. A critical feature would be the development, design and integration of various useful sensors in the industrial applications ( Li et al., 2020b ), to form integral and effective management systems. More intense research efforts are needed towards an efficient application of IoT technologies in the industry and to better understand how IoT technologies could be implemented in specific industries where benefits would be possible. Progress is crucial in the sense of how to connect different industrial sensors, use and process the collected various data to enable enhanced industrial processes, i.e. ensue for instance smart IoT based Computer-Integrated Manufacturing, ( Chen et al., 2020b ).
General concept of IoT industrial application ( Aazam et al., 2018 ).
1.2.2. IoT in smart city concept
The role of IoT technologies in the smart city concept ( Janik et al., 2020 ) is crucial to bridge the already mentioned global infrastructural challenges in cities, which are linked with the current increase of population in cities. IoT technologies in smart cities would enable the utilization of different devices, which would increase the life quality in cities as well as the efficiency of different daily services such as transportation, security (surveillance), smart metering, smart energy systems, smart water management, etc. Different sensing devices would receive information, which would be processed towards efficient and useful solutions. The main benefit of IoT technologies in smart cities would be directed to the early detection of different problems or infrastructural faults (such as issues with traffic jams, energy supply, water shortage, security incidents, etc.). In smart cities, many sensors are installed and linked with many other devices over the internet which gives information to the users as for instance parking spaces, any malfunctions, electrical failure and many other issues. Developing these technologies would help in leading the cities towards smart grids, smart healthcare, smart warehouses, smart transportation, smart waste management, smart communities, etc. Different implementation challenges towards the smart city concept exists, Fig. 6 and should be solved for various applications, ( Fig. 7 ).
Different challenges in Smart City concept ( Bhagya et al., 2018 ).
Various smart home management systems ( Zhou et al., 2016 ).
The most present implementation challenges are linked with the efficient integration of different sensing technologies, development of a suitable network infrastructure, education of population, investigation of the sustainability aspect, such as carbon footprint, etc.
The application of IoT technologies in smart homes, ( Moniruzzaman et al., 2020 ), within the smart city concept allows for an increase in the life quality within residential facilities, bringing novel and attractive technological solutions. Both, energy and fund savings could be reached with more efficient time management, which is a valuable feature in our present economic system. Different control options are possible within the smart home concept and enable an efficient integration of renewable energy technologies in homes ( Stavrakas and Flamos, 2020 ), and their efficient balancing (efficient supply and demand).
1.2.3. IoT in agriculture
Efficient agriculture production is a necessity for our population to prevent the potential lack of food resources in future terms caused by different factors, ( Hussain et al., 2020 ). The first factor is constant population growth, as already emphasized, the second is linked with climate change issues ( Yang et al., 2020 ), which is causing a reduction in the yields of important crops, or some areas are even becoming unsuitable for efficient agriculture production. The food waste issue is one of the major problems ( Keng et al., 2020 ), since it has become a global problem, especially in developed economies. It is estimated that more than 28% of available agriculture areas is ‘‘reserved’’ for food waste and unfortunately more than 800·10 6 people are currently hungry, ( Fao.org, 2020 ). The implementation of IoT technologies in agriculture can certainly help to secure sufficient food demands and increase the efficiency of agricultural production processes in general. Various useful data about crops could be collected and used for yield monitoring and the detection of potential diseases in advance that can significantly reduce the yields of specific crops. The monitoring of soil and nutrients would rationalize agricultural production processes and lead to water savings that are precious in some specific geographical regions, which could be utilized through smart irrigation systems, ( Xin et al., 2020 ). A more precise seeding could also be ensured and fertility crop management in general, Fig. 8. There are some issues linked with the efficient application of IoT technologies in agriculture production. Different sensing and monitoring technologies should be developed and a better education of farmers should be provided (i.e. development of standard education modules for farmers). Due to a large quantity of collected data, farmers could be potentially overwhelmed, ( Ec.europa, 2017 ). Therefore, there is a necessity for the development of standard trainings (education modules) for farmers coupled with the development of more user-friendly software solutions.
IoT in agricultural production from farmer’s perspective.
The application of IoT technologies in the agricultural sector would lead to advancements that could drastically modify current production procedures in agriculture, ( Shafi et al., 2020 ) ( Fig. 8 ).
1.2.4. IoT in waste management
Waste management towards a circular economy concept ( Fan et al., 2019 ) is a vital current population problem, where there is certainly a role for IoT technologies that could help provide more efficient waste management in specific areas ( Voca and Ribic, 2020 ) and recycling of different limited resources, ( Qiu et al., 2020 ). Currently, various technological solutions are being developed to support the smart waste management concept, ( Das et al., 2019 ). Some of them are already available on the market for wide implementation, ( Iot.farsite, 2020 ). The developed solutions are mostly directed towards the smart monitoring of waste bins ( Dhana Shree et al., 2019 ), i.e. bin filling level detection, waste temperature and fire detection, bin vibration occurrence and bin tilt, presence of waste operators, waste humidity, bin GPS location etc. In general, smart waste management systems, can be effectively supported by IoT devices, Fig. 9 . IoT technologies could also be used for the smart coordination of waste trucks ( Idwan et al., 2020 ) and efficiency waste utility companies could be ensured in that manner, which would be followed by a reduction of harmful emissions (pollutants) created by garbage trucks, ( Kozina et al., 2020 ). From the perspective of smart technologies, the proper and IoT based waste management of electronic waste is very important ( Kang et al., 2020 ) to secure sufficient raw resources to produce electronic equipment as already highlighted. IoT technologies could also be used for the reduction of food waste through intelligent appliances and a developed management structure in that sense, ( Liegeard and Manning, 2020 ).
IoT in smart waste management system, ( Quamtra, 2020 ).
Innovative IoT based technological solutions are expected to be developed in upcoming years, especially from a smart city concept perspective and that could support smart waste management systems and a circular economy concept.
1.2.5. IoT in healthcare
One challenging implementation field of IoT technologies has been detected in the healthcare system in general, through the e-health concept, ( Farahani et al., 2020 ). An increase in the service quality of healthcare systems could be utilized through IoT support (mainly collection of patient health data) and finally with the improvement of patient safety and care since it could also lead to an increase in patient life expectancy. There is an enormous potential in smart medical devices for different purposes ( Papa et al., 2020 ) that can be utilized for the monitoring of various vital and valuable human functions such as heart rate, skin temperature, movement monitoring, etc. Remote health monitoring is also an interesting perspective that could be utilized with the proper support of IoT devices and products. The prediction of different symptoms and prevention of potentially life hazardous states and diseases could generally be enabled, ( Muthu et al., 2020 ). Assistance to the elderly could also be ensured by monitoring a patient’s general health condition and nutrition status ( Nivetha et al., 2020 ), that would be supported via IoT devices. Rehabilitation after a serious disease could also be efficiently supported with IoT technologies, especially in cases of home rehabilitation circumstances, ( Bisio et al., 2019 ). One of the main issues and challenges in this specific IoT application field would be to ensure proper cyber safety due to potential attacks that could occur within healthcare monitoring systems, ( John et al., 2019 ). Significant progress in upcoming years is expected in the field of software development for health care systems, i.e. especially in hospitals. Namely, different devices could be linked via advanced software solutions as for example MRIs or CT devices and connected with laboratory data to create a smart hospital information system. The previously mentioned approach would allow for the better treatment of patients, detection of medical priorities and support for medical staff in monitoring and therapy decisions. IoT systems could also be used in hospitals for the efficient maintenance of a large number of medical devices ( Shamayleh et al., 2020 ). Equipment costs could be reduced in hospital systems due to the early detection of severe equipment malfunctions that could affect the accuracy of specific readings from medical devices. The development of smart and based IoT solutions in healthcare systems could also be very useful in the case of severe global pandemic states (data collection and fast data diversity, resources of medical staff and resources, medical triage, etc.), such as is the recent corona virus situation that has severely threatened the global population, ( WHO, 2020 ). The healthcare sector is probably one of the most challenging areas for IoT, thus important progress is expected in the upcoming year with serious benefits for the population.
1.2.6. IoT in transportation
Transportation modes will be significantly changed in upcoming decades, ( Jonkeren et al., 2019 ), especially due to the expected rising implementation of electric cars on the market, ( Capuder et al., 2020 ). The upcoming ban of Diesel based vehicles due to environmental issues ( Li et al., 2020c ) and finally development of alternative transportation technologies, such as hydrogen based vehicles for example ( Ajanovic and Haas, 2019 ), would change the shape of future transportation systems. In general, there is a demand for more environmentally suitable transportation options that are already being gradually developed with an expected penetration on the market. A necessary development of transportation infrastructure is needed for specific vehicle technologies to ensure desirable vehicle autonomy. Nowadays, the IoT emerged in the ‘‘internet of vehicles’’ concept ( Shen et al., 2020 ), which just proves its potential in this important area. The most significant IoT application area is in the case of the smart car (vehicles) concept, ( Chugh et al., 2020 ). The smart car concept considers the utilization and optimization of different internal functions in the car that are supported by IoT technologies. The application of IoT would upgrade driver experience and increase in comfort and safety. Specific data are collected in the smart car and associated with the main operating parameters such as tyre pressure, fuelling, early detection of potential failures, regular maintenance indicators, etc. In general, improved service as well as added value for customers could be obtained with a targeted utilization of IoT technologies, which finally can improve competition in the automobile industry between vehicle manufacturers. The challenging aspect of IoT application is in the case of autonomous vehicles, ( Padmaja et al., 2019 ). Location, direction as well as a planned path of the autonomous vehicle could be efficiently supported with IoT in general as well as the monitoring of safety systems for autonomous vehicles, ( Bylykbashi et al., 2020 ). The most important issue with autonomous vehicles is the prevention and avoidance of crash vehicle accidents, which could be solved with a targeted utilization of IoT devices, ( Abdou et al., 2019 ). Smart parking is also currently one of the most developing IoT areas when considering the transportation sector in general terms. Different research efforts are provided in that sense with the main goal being to enable the latest status of available parking space, control and monitoring of different useful parking space information in real time, ( Luque-Vega et al., 2019 ). Again, the development of sensor technologies, i.e. smart parking sensors is very important to enable efficient and accurate service, ( Perković et al., 2020a ). The maintenance and failure prevention of different vehicles could also be supported by IoT ( Saki et al., 2020 ), which could improve security and the lifetime of vehicles. Taking all the previously addressed into account, IoT technologies could completely change the driving experience and generally improve the quality of transportation systems from various aspects.
1.2.7. IoT in smart grids and power management
Energy transition ( Biresselioglu et al., 2020 ) has become a necessity due to the potential shortcomings of fossil fuel resources in future terms and for the reduction of different pollution impacts that are associated with the utilization of various fossil-based technologies, ( Bielski et al., 2020 ). Since a more intense implementation of renewable energy technologies has already been occurring, the efficient and advanced power management of electric grids has become an important aspect. Efficient demand side management with accurate and flexible smart metering technologies are key factors to enable smart power management in smart grids, ( Mendes et al., 2020 ). The most important role of IoT technologies in smart grids is to save electricity ( Rishav et al., 2019 ), with efficient distribution of electricity, Fig. 10 . The collection of specific grid data through IoT devices, and later their analysis with the proper software, could help improve grid reliability and efficiency. The economic aspect of electricity could also be improved with IoT due to the already mentioned efficiency improvement as previously highlighted. Useful benefits could be ensured both for customers and service providers.
Concept of smart grids ( Tuballa and Lochinvar Abundo, 2016 ),
A demand side management in households is also an important application area of IoT, ( Rahimi et al., 2020 ). Homes are typically equipped with different appliances that are becoming more advanced, creating the possibility for an efficient operation with the regulation of IoT, ( Tawalbeh et al., 2019 ). The efficient and smart forecasting of electricity demands for households could also be effectively supported by IoT technologies, ( Nils et al., 2020 ). An expected higher penetration of renewables in households through hybrid energy systems as an example ( Gagliano et al., 2019 ), would also require a smart operation strategy that could be utilized by IoT through integrated smart nano-grids, ( Kalair et al., 2020 ). A growth of IoT products and technologies in smart power management is expected to enable accurate forecasting and different load strategies in the case of renewable generation, ( Pawar et al., 2020 ). The elaborated main issues and challenges above just reflect the importance of IoT devices in smart grids and power management.
1.3. Review methodology
By addressing all the above raised general challenges towards an efficient and suitable implementation of IoT technologies, it is evident that more intense research efforts are needed to lead to further advancements in this dynamic research topic, with a strong application potential. A synergy of different research efforts in the field, mainly focused on the targeted topical area is needed. The main contribution and novelty of this review editorial is in line with that. Further main topical areas are addressed in the herein review introductory editorial;
- - IoT technologies in sustainable energy and environmental issues,
- - IoT enabled Smart City
- - E-health – Ambient assisted living systems
- - IoT technologies in Transportation and Low Carbon Products
The main objective of the herein presented review editorial is to address and discuss the latest advancements in the above specified and key IoT application areas. This review editorial serves as an introduction to the Virtual Special Issue (VSI) of JCLEPRO devoted to the 4th International Conference on Smart and Sustainable Technologies (SplTtech 2019) held on 18–21 June 2019, in Bol (Island of Brač) and Split, at the University of Split, (Croatia). The herein presented introductory review editorial was directed to the selected and accepted publications from the international conference SpliTech2019 and published papers were divided into four main topical areas as already specified above. Overall 38 papers were initially selected and invited for potential inclusion in the VSI SpliTech 2019. After conducted peer-review process, based on the JCLP procedures, 29 of them were selected for inclusion in the VSI SpliTech 2019. Authors from following countries have contributed VSI SpliTech 2019: China, India, Australia, Canada, Italy, Croatia, Serbia, Greece, Poland, Czech Republic, Spain, Cyprus, Turkey, Norway, Iran, Germany, Brazil, Malaysia, Pakistan, Dubai and United Kingdom. Besides the selected VSI SpliTech2019 works published in the JCLEPRO, the other relevant and latest works from the existing literature in the field were also addressed using the Scopus database, ( Scopus, 2020 ). Based on the conducted review as well as selected contributions in this VSI the key issues were identified, discussed and highlighted in the conclusion section.
2. IoT technologies in sustainable energy and environment
The rapid development of information technologies caused in one sense the necessity for ‘‘energy digitalization’‘. The increasing application of renewable energy technologies and development of efficient policies will be key points in upcoming decades to be able to secure global energy transition goals, ( Tzankova, 2020 ). Referring to the previous, the development of alternative renewable energy sources would also be valuable, ( Nižetić, 2010 ). Different energy scenarios or options have been considered in recent years involving a high share of renewables via hybrid energy options ( Nizetic et al., 2014 ), or for instance the possible application of alternative energy sources such as hydrogen technologies in different implementation fields ( El-Emam et al., 2020 ), or vehicle applications ( Matulić et al., 2019 ). The focus of the research is to investigate the techno-economic viability of different energy concepts in order to secure a suitable mix of energy technologies that would support an efficient energy transition. An improvement in the energy efficiency of different renewable energy technologies is also important, especially in the case of photovoltaics ( Grubišić-Čabo et al., 2019 ) and wind generation technologies ( Marinić-Kragić et al., 2020 ), to secure large scale projects. The efficiency of specific production processes ( Giama et al., 2020 ), is also vital and certainly needs to be carefully investigated and analysed, to reduce energy intensity and provide a circular economy concept in specific application areas, ( Xu et al., 2020 ). The main research efforts should be directed towards the upgrade of energy saving technologies followed with the increasing utilization of renewable energy sources, ( Klemeš et al., 2019 ). Recent technological progress in the field of IoT technologies has enabled different opportunities for the possible application of IoT concepts in the energy sector and environmental protection to secure a sustainable development.
Energy and environment are two of the most important elements of Smart Cities and are very often closely interrelated concepts. The available challenges in energy management to use and generate energy in the most efficient manner possible, and the development of a sustainable energy structure can take advantage of Internet of Things (IoT) and Internet of Energy (IoE) technologies, Fig. 11 ( Mohammadian, 2019 ) or in the case of battery charging protocols ( Fachechi et al., 2015 ).
IoE architecture ( Mohammadian, 2019 ).
The climate change and global warming impose a paradigm shift in the exploitation of resources and in more efficient energy resource management: production, distribution and consumption, as an integral part of this vision. The energy transition must point to an infrastructure change at the center of which there are the so-called smart grids. With the advent of smart grids and new technologies, the energy industry is inexorably changing. The most interesting aspect is that smart grids ensure flexibility in demand and allow consumers to participate in the energy system, as prosumers. Smart grids exploit digital and innovative technologies to manage and monitor the transport of electricity from all sources of generation to promptly, quickly and effectively satisfy the demand of end users. Smart grids are raising reliability, system resilience and stability, and minimizing disruptions, costs and environmental impacts. Some of these new technologies such as Distributed Generation (DG) and microgrids provide energy locally, creating larger and more reliable networks and reducing the line overload. Energy storage complements the energy from renewable sources while microgrids help reduce any blackouts by providing energy locally. Unlike the existing power system of a unidirectional system, which distributes electricity generated from a power plant to the consumer, the microgrid is equipped with a local power supply and storage system centered on independent distributed power sources. It is an energy network that can connect with an existing power system as needed and the self-sufficiency of energy such as electricity and heat by using multiple distributed power sources independently. In addition to giving owners the ability to generate their own energy, microgrids also reduce the dependency on energy providers by helping reduce costs and avoid peak usage charges. The microgrid can produce revenue if it were to produce a surplus of power, which could be sold to the energy provider. Recent works in the energy related field are discussed in the upcoming section of the paper to highlight IoT implementation areas and clarify the benefits in specific engineering applications.
In microgrids, IoT technologies are introduced mainly to realize a smart system able to autonomously schedule loads and/or detect system faults and then improving the efficiency of the energy consumption. The work ( Nayanatara et al., 2018 ) proposes a renewable energy based microgrid management strategy to use renewable energy (solar energy from a photovoltaic panel and wind energy from a wind turbine) effectively reducing the energy usage from the power grid. IoT technologies are used to realizing a smart scheduling algorithm able to control schedulable loads as per the needs of the consumer. Authors demonstrate that the proposed energy management system installed in an institution enables low power consumption and reduced costs. In ( Sujeeth et al., 2018 ) an IoT-based automated system that constantly monitors the current and voltage flowing through various branches of a DC microgrid, detects and controls the fault clearance process during fault conditions that has been developed. The system is capable to alert the user during overcurrent faults, ground faults and short circuit faults. As the operation of a microgrid is automated, the need for human decision making is eliminated and the minimum reaction time to react to fault conditions is drastically reduced. The work ( Majee et al., 2018 ) is also focused on the issue of fault management within a microgrid exploiting the IoT. The concept of IoT is used to solve the issues of microgrid reconfiguration occurring due to faults, changing energy usage patterns and the inclusion and removal of distributed energy resources.
Smart grids can automatically monitor energy flows and adapt to changes in energy demand and supply in a flexible and real-time manner. These smart systems can benefit from technologies such as machine learning ( Chou et al., 2019 ) and artificial intelligence ( Bose, 2017 ) to perform predictive analyzes and better configure all the devices. To do this, however, smart grids require adequate and equally intelligent measuring instruments. Here, smart metering tools could be efficient solution, reaching the consumers and suppliers, providing them with information on consumption in real-time. With smart meters, consumers can adapt - in terms of time and volume - their energy consumption to different energy prices during the day, saving on their energy bills by consuming more energy in periods of lower prices. In this perspective, the possibilities generated by improved digitization and sensorization, utilizing to the Internet of Things solutions, has led many research works to focus on realizing innovative IoT-based hardware and software solutions. These solutions are capable of providing real-time information about the quality usage of appliances, data consumption, and energy flow information ( Morello et al., 2017 ). present an interesting study on the role of advanced smart metering systems in the electric grid of the future through the realization and the experimental validation of a smart meter, Fig. 12 . The cost effective three phase smart energy meter, IoT enabled, multi-protocol and modular, capable to collect, process, and transmit several electric energies related information, mainly focused on consumer-side, to any smart energy control system was proposed by ( Avancini et al., 2018 ), Fig. 13 .
Proposed smart power meter, ( Morello et al., 2017 ).
Photo of created IoT enabled smart energy meter ( Avancini et al., 2018 ).
Several solutions are also based on the use of the Arduino platform ( Arduino, 2020 ) and a few sensors for the realization of low-cost smart meters ( Patel et al., 2019 ) or for instance Arduino based solutions ( Saha et al., 2018 ). Although smart grids are fundamental elements when it comes to energy sustainability, it is reductive to identify the concept of smart energy only in them. In fact, smart buildings also play a crucial role. The energy efficiency of building structures using smart technologies provides an increasingly intelligent management of resources, avoiding waste, improving the life quality of people and making the buildings themselves more resilient in the face of current climate changes. Thanks to building automation and IoT not only individual buildings but also entire neighborhoods can be controlled remotely from an energy point of view and in terms of the security. For example, it is possible to carry out checks on air pollution remotely ( Becnel et al., 2019 ), monitor fire systems ( Cavalera et al., 2019 ) or, furthermore, immediately detect any intrusion by outsiders ( Dasari et al., 2019 ).
Smart buildings are able to monitor actual energy needs, optimizing consumption and therefore counting not only on green energy, but also on a high degree of energy efficiency. The virtuous process that passes from smart energy allows to count on Nearly (Net) Zero Energy Building (NZEB) ( Rushikesh Babu and Vyjayanthi, 2017 ) and on a wider energy sustainability.
The most common use of IoT for energy and environmental sustainability is in the home automation systems, which allow homeowners to live comfortably and manage energy consumption through connected devices. In this field, numerous applications have been implemented and, despite the common goal of creating an Energy Management System (EMS) for home, the techniques used to achieve it can be very different. For example ( Li et al., 2018 ), propose a self-learning home management system that exploits computational and machine learning technologies, Fig. 14 . The proposed system has been validated by collecting real-time power consumption data from a Singapore smart home. In ( Al-Ali et al., 2017 ), an EMS for smart home is realized exploiting off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and meet consumer demands. In this work, the proposed system has been validated realizing a case study based on the use of HVAC (Heating, Ventilation and Air Conditioning) Units. Smart energy solutions such as those analysed provide real-time visibility of consumption and billing data, helping consumers to save resources, while energy and service companies can better balance production to meet actual demands, reducing potential problems. As the main effect, the energy consumption of families is reduced, also decreasing our impact on climate change.
Self-learning home management system architecture ( Li et al., 2018 ).
In addition to buildings and homes, industrial facilities and enterprises also deal with the adoption of innovative energy efficiency solutions to optimize resource consumption and reduce costs, but they need to evaluate a high number of factors to adopt the best energy efficiency measures. The work ( Suciu et al., 2019 ) proposes an IoT and Cloud-based energy monitoring and simulation platform to help companies monitor energy production and consumption, forecast the energy production potential and simulate the economic efficiency for multiple investment scenarios.
The concept of sustainability is increasingly linked to that of circular economy, which is now considered the key to this new paradigm. Unlike the traditional linear economy, based on the so-called “take-make-dispose” scheme, which provides for a complete utilization of resources, the circular economy model promotes reparability, durability and recyclability. In practice, the circular economy aims to minimize waste through reuse, repair, refurbishment and recycling of existing materials and products, focusing attention on designs that last over time. In this system, the IoT is considered an essential element, as it offers new opportunities in various sectors, such as manufacturing, energy and public services, infrastructure, logistics, waste management, fishing and agriculture. Especially in the field of waste management, research has made great strides through the creation of innovative systems capable of concretizing the concept of digital economy. In the work ( De Fazio et al., 2019 ) the activities related to the research project called POIROT were discussed, which exploit innovative hardware and software technologies, aiming to realize a platform for the inertization and traceability of organic waste. In detail, the main project objective is to realize a targeted transformation, through technological processes, regarding the organic fraction of urban solid waste, into inert, odorless and sanitized material, identified and traced to be employed for building applications or as thermal acoustic insulator, Fig. 15 .
Architecture of proposed identification and traceability system, ( De Fazio et al., 2019 ).
Several works propose solutions to support waste management at a domestic level, simplifying the waste separation to avoid problems due to improper waste management including hazards for human health or environmental issues. For example ( Al-Masri et al., 2018 ), propose a server-less IoT architecture for smart waste management systems able to identify waste materials prior to the separation process. This allows reducing costs related to the waste separation process from hazardous materials such as paint or batteries ( Kumar et al., 2017 ). propose a hygienic electronic system of waste segregation. The proposed approach eases the segregation of wastes at source level and thereby reducing the human interaction and curbs the pollution caused by improper segregation and management of wastes at source level.
The role of IoT supported smart meters was considered in the work ( Mendes et al., 2020 ) to address different demand side management scenarios. The novel and adaptive compression mechanism was proposed in the same work to improve the communication infrastructure for the given case, i.e. complete controlling structure, Fig. 16 . The proposed mechanism can reduce the quantity of data sent to utility companies and can automatize energy consumption management.
Proposed general controlling structure ( Mendes et al., 2020 ).
The proposed and tested control solution showed to be efficient with respect to the considered application, since compression rates were satisfactory and the proposed concept showed potential for other applications. The demand side management of a hybrid rooftop photovoltaic system was discussed in ( Kalair et al., 2020 ) where the system was integrated in a smart Nano grid. The smart monitoring system was presented in detail for residential purposes, together with a developed experimental setup that contains specific electronic components, Fig. 17 . The developed controller can automatically detect any frequency and voltage changes and link them with specific loading patterns. The proposed solution demonstrated efficiency since the power supply reliability was up to 97%. The proposed home management system could lead to the reduction of carbon footprints in the case of residential facilities.
Experimental setup with pre-processing unit (a) and smart controller (b) ( Kalair et al., 2020 ).
A machine learning-based smart home energy system was investigated in ( Machorro-Cano et al., 2020 ), using big data with the support of IoT. The home automatization system was coupled with IoT devices that enabled energy savings for the given purpose. A machine learning algorithm was used to study user behaviour and was later linked with energy consumption, i.e., with the proposed approach, specific user patterns were revealed. The developed monitoring system, Fig. 18 allowed specific recommendations to lead towards an improvement of energy efficiency in households, which were somehow personalized for the specific household. The system was successfully validated via the provided case study where the main strength of the conducted research was the personalized approach for the specific household. A step further could be to network and balance other households in the specific building facility. The importance of the BIM (Building Information Modelling) systems was discussed and analysed in the review paper ( Pantelia et al., 2020 ). An overview of the recent works focused on the building smart operation was elaborated in detail with use of IoT technologies. In the same work the renovation projects were also tackled as well as interoperability problems caused by data sharing with respect to the BIM related applications.
Concept of proposed IoT supported smart home system ( Machorro-Cano et al., 2020 ).
An application of smart wearable sensors was reported in the study ( Pivac et al., 2019 ) that were used for the monitoring of thermal comfort data as well as for the modelling of occupant metabolic response in office buildings. The smart and IoT supported monitoring system allowed the collection of useful data from the wearable sensors. The readings helped for the better understanding of thermal comfort issues in office buildings from a personalized thermal comfort point of the view. The experimental readings were compared with a subjective response from the occupants, where a successful modelling of personal metabolic responses was enabled with an accuracy of over 90%. Industrial facilities could also be improved with the implementation of IoT technologies as already briefly addressed in the introduction section. Legislation support is important to ensure smart electricity utilization in the households, especially from the perspective of the smart city concept. Study ( Grycan, 2020 ) discussed legislative for electricity consumption for the case of the Polish residential sector. Lack of legislative was detected and mainly in the smart metering solutions that are slowing down development of the smart infrastructure. There is necessity for the new regulations to ensure adaptability to the novel desired goals towards smart cities. Development of the novel business models is important to ensure smart driven business in the energy sector. The case of the smart energy driven model was elaborated in ( Chasin et al., 2020 ) as well as implications and necessary changes in the energy sector. Eight smart business models were discussed with introduction of desired changes. Presented knowledge and development business scenarios could be useful guideline for energy utility companies. The possibility of IoT based smart solutions was discussed in the review paper ( Bagdadee et al., 2020 ), where the focus of the work was on IoT-based energy management systems in the industry. IoT based energy management systems were elaborated for industrial applications as well as for smart energy planning in industrial facilities. The energy management systems in factories were addressed from a perspective of energy demand and supply. The focus of IoT applications could also be used on a level of single or multiple devices or appliances. The scheduling and optimal power management of the transformers was analysed and discussed in ( Sarajčev et al., 2020 ). The Bayesian approach was applied to detect an optimal controlling strategy to ensure benefits for power utility companies. The proposed and demonstrated model can predict the transformer health index with an accuracy of about 90%. The solution could be applied on the fleet of the power transformers where with the application of IoT technologies, further savings could be ensured for the specific application. The efficiency of the lighting system could also be improved with IoT devices. The work ( Mukta et al., 2020 ) discussed and reviewed the possible application of IoT technologies for the energy efficiency improvement of highway lighting systems. The results of the conducted review revealed that the development of smart and IoT supported highway lighting systems lack a systematic approach, quality and comprehensiveness. Possible framework was proposed to bridge the mentioned gap and secure an efficient pathway for the improvement of energy efficiency in IoT based lighting smart and green highway systems. The necessity for the environmental suitability of the proposed smart lighting system was also raised in the same study and noted as an important factor that needs to be further investigated. Energy harvesting is also interesting topic and closely linked with the possible application of IoT technologies, especially since IoT devices require energy for their operation. An underwater piezoelectric energy harvesting system was discussed in ( Kim et al., 2020 ) for the case of autonomous IoT sensor production. The proposed solution was fully designed and provided in the form of a prototype and demonstrated an autonomous energy source that could be further linked with IoT devices. The harvesting of waste energy could also be considered with the implementation of IoT devices. The possibility for waste energy harvesting supported by IoT was addressed and discussed in ( Kausmally et al., 2020 ) for the case of an industrial chimney. The complete design procedure was reported, i.e. the conceptual approach for the waste heat recovery where the prototype was successfully developed and demonstrated. Energy storage systems are also interesting for the application of IoT technologies. A renewable energy storage system was analysed in ( Sathishkumar and Karthikeyan, 2020 ), where a power management strategy was supported by IoT. The optimal design of a hybrid energy system coupled with energy storage was discussed based on solar and wind renewable energy resources.
The IoT approach allows successful monitoring and managing of complex energy systems. The main advantage of IoT for the considered application is the energy efficiency improvement, better synchronization of different energy systems and improvement of the economic aspect. A significant development of IoT products would lead to a rapid increase of big data that are usually processed by data centres. The energy load of data centres is increased, so efficiency improvements are necessary in the case of data centres to minimize load power as well as utilization of other limited resources. The issue related to data centres, power demands and the possible application of IoT technologies in order to reduce the mentioned unwanted impacts was discussed in ( Kaur et al., 2020 ). The authors proposed a specific framework in the same work that is applicable for data centres and could lead to efficiency improvement of over 27% (proposed approach was based on empirical evaluations).
IoT technologies could also be successfully implemented in a circular economy concept as above already mentioned, especially in smart waste management systems and environment protection as already mentioned. The role of IoT technologies in e-waste was discussed in ( Kang et al., 2020 ) for the case of the Malaysian recycling sector. A novel smart waste collection box was designed together with a user friendly mobile application, Fig. 19 . The concept was successfully demonstrated. The developed solution could be further optimized and fitted for possible market implementation. A discussion of possible IoT framework, based on the developed IoT supported smart e-waste bin was elaborated for the Sunway city in Malaysia. The proposed approach could be a helpful guideline for other cities. The remaining issue with the proposed concept is its economic feasibility that should be further investigated via a detailed user survey, detecting user willingness for the acceptance of the proposed concept. The innovative IoT supported platform for the transformation of organic waste into inert and sterilized material was reported in ( Ferrari et al., 2020 ). The specific Arduino-electronic platform was developed to control process parameters and link them with user responses and traceability. Novel and low cost sensors were developed and successfully applied for the given purpose. The proposed prototype of the device was presented and was used for the mechanical treatment of waste. The developed IoT supported framework for the identification and traceability of products was presented in Fig. 20 .
Prototype of smart e-waste bin ( Kang et al., 2020 ).
Conceptual IoT supported framework for waste processing ( Ferrari et al., 2020 ).
The implementation of IoT technologies in a circular supply chain framework was elaborated in ( Garrido-Hidalgo et al., 2020 ) for the waste management of Li-ion battery packs from used electric vehicles. A novel and IoT supported supply chain framework was proposed, which is compatible with the information infrastructure. The approach could be further used for the recovery process of Li-ion batteries. Due to a planned increase in electric car fleets globally, intensive research was also directed for the potential usage of IoT technologies for the smart charging of electric vehicles. Real time IoT based forecasting applications were proposed in ( Savari et al., 2020 ) for a more efficient charging process of electric vehicles. The application allowed better scheduling management where the waiting time was minimized, which improved the overall charging economy as well as charging time.
Environmental protection and sustainable behaviour could also be improved with the targeted application of IoT technologies. In the study ( Irizar-Arrieta et al., 2020 ), long-term field investigation was presented with the main goal being to investigate how IoT technologies could help ensure the sustainable behaviour of users in office building facilities. The results of the conducted directed study could lead to the improvement of energy efficiency at workplaces with IoT utilization in different aspects. The impact of IoT technologies on a sustainable perspective and society was addressed in ( Mahmood et al., 2020 ). The study was focused on addressing the impacts of home systems on the environment and sustainability in general. A survey was conducted for specific users and the investigation showed that the impact of home automatization on sustainability and environment is significant. However, the environmental effects should be discussed in more detail and quantified to get realistic indicators that would later be used for sustainable planning.
Besides the obvious potential impact of IoT technologies to the environment, IoT products could on the other side be used for environmental protection. The design and concept of a systematic framework for the massive deployment of IoT-based PM (Particulate Matter) sensing devices was elaborated in ( Chen et al., 2020c ). The proposed framework was applied for the monitoring of air quality. Compressed spatiotemporal data were used and that allowed for the efficiency improvement of air quality monitoring systems, energy savings and improved data saving ratio. In order to improve the interoperability between different sensor networks, as well as data sources, a novel IoT data framework was proposed in ( Duy et al., 2019 ). The proposed analytical framework was used as a useful tool to improve the data management of environmental monitoring systems. The developed framework enabled a more efficient utilization of the gathered environmental data and improved knowledge extraction later. IoT platforms could also be used for environmental planning as it was demonstrated in the study ( Wu et al., 2019 ). In the conducted research, a building information model was integrated successfully with IoT and used for environmental planning for environmental protection reasons. Moreover, the system was used for environmental protection in a specific construction project (tunnel utility). Different impacts to the environment were monitored during the construction project such as dust falling control, temperature monitoring, visual monitoring etc. The overall findings directed that the proposed IoT supported system showed to be effective for the considered application. The application of an IoT based data logger was presented in ( Mishra et al., 2020 ), for the monitoring of equipment for environmental protection. The developed monitoring system ensured accurate and reliable work of the equipment used for the environmental protection. Potential equipment faults were detected in advance (prevention of serious failure), the equipment energy consumption was rationalized and scheduled maintenance was enabled. The accurate prediction of particulate matter (PM 2.5 ) concentrations is very important, especially in urban areas. Usually, there is a network of sensors used for the monitoring of PM 2.5 concentrations but they are not well connected and harmonized in some situations, which is vital. An IoT framework was used, together with a fusion technique, to improve the data utilization from the PM measuring stations in the work ( Lin et al., 2020 ). A novel multi-sensor space-time data fusion framework was proposed that ensured better accuracy, i.e. a more reliable model was ensured with a higher spatial-temporal resolution. Regarding the current progress of specific application areas in IoT devices for environmental protection, it can be conducted that the studies were mostly focused on air quality monitoring. Water-Energy-Carbon (WEC) nexus was analysed in detail for EU27 countries, in the recent work ( Wang et al., 2020 ) by implementation of the Environmental Input-Output model (EIO). Study was important since contributed to the better understanding of the environmental performance in EU27 and could serve as important basis for future considerations or planning for policymakers.
Based on the previously conducted overview of latest research findings related to the application of IoT technologies in sustainable energy and environment, the further main findings could be highlighted:
- - IoT technologies are intensively investigated from a perspective of smart monitoring in different devices or engineering components that are associated with energy applications. Better usage and networking of various collected data could lead to noticeable efficiency improvements, energy savings, improved safety, improved equipment maintenance and finally the general improved operation of devices in different engineering applications,
- - The economic aspect associated with the application of IoT technologies was not addressed in most studies, which is a significant drawback,
- - The environmental impacts associated with the implementation of IoT technologies for specific use were not addressed, which is serious and an important issue that should be carefully considered and investigated when discussing specific IoT concepts. Moreover, an integral techno-economic-environmental conceptual approach (TEE) should be applied when considering an IoT application for specific cases,
- - The main advantages (benefits) of IoT technologies enable a personalized approach in specific engineering applications such as smart homes (level of single user), which lead to different possibilities for both energy and fund savings,
- - There is significant potential in IoT technologies for environmental protection; however, rare studies have been conducted in that sense. More intense research efforts are needed in that direction to be able to utilize all the potential benefits of IoT technologies and improve the environmental suitability of IoT in one sense,
- - The waste management and circular economy concept could be well supported with IoT technologies, where the main issue is the development of integral and conceptual smart waste management frameworks that would efficiently support the circular economy concept in different economies.
3. IoT enabled smart city
To enable the IoT-based smart city concept, Fig. 21 , is described in the form of a tree that can be considered to further understand what the possible applications or functionalities the IoT-enabled Smart City can provide. The branches of the given tree are dedicated to applications, wherein the leaves of the given branch are dedicated to the functionalities that each application can have. As the more leaves a branch contains, the more functionalities it has. Fig. 16 represents the different functionalities of a smart parking system for instance. Further on, for example, smart homes can have many functionalities: smart metering (electricity, water consumption, gas monitoring), smart lock control, smart room temperature monitoring, smart kitchens and other appliances, etc. The root of the given tree (enabler and information source of these systems) is dedicated to the hardware whose system uses to accomplish any of the given possible functionalities. This section considers an overview of the most important hardware technologies, and software architectures that can enable and present functionalities for different applications in the smart city concept.
Generalized concept of IoT enabled Smart City Architecture ( Perković et al., 2020b ).
3.1. Hardware overview and state-of-the-art
To enable Smart Cities, an infrastructure that uses sensing hardware acting as an information source is of crucial importance. As this sensing hardware is located in remote areas, often without access to an electrical network, an almost zero-energy use is needed and therefore can prolong battery lifetime and possibly enable self-powering through ambient power sources, e.g. solar cells. This is crucial for improving the usability of the whole system as a battery replacement in these circumstances is difficult, expensive and a time-consuming activity. To understand power consumption issues, an overview of state-of-the-art technologies to build the hardware is provided.
A standard sensing node, presented in Fig. 22 is consisted of a sensor component that delivers the sensed information to a microcontroller unit (MCU) for its further processing. To reduce power needs, the node is usually equipped with a related power management unit, while there is a given power source. Once the MCU acquires the data from the sensor, it gives data to a radio unit that uses an antenna to transmit the data over a wireless channel. In the next sections, the components are described in depth by referencing the relevant literature, while the specific original work was done in current technology investigations that can enable these functionalities.
Block scheme of standard sensing node architecture in IoT enabled Smart City.
Sensors vary in terms of design and functionalities. A good overview of sensing technologies, and its power consumption is given in Fig. 23 . It can be noticed that each of them has its own power consumption pattern, where the more functionalities they have, the more consumption will appear. Using it in an optimal way is of the highest importance for reducing battery lifetime.
Most popular sensors and their power requirements in active and power-down (i.e. sleep mode, Perković et al., 2020b ).
3.2. Efficient IoT radio units
To achieve data transmission, a critical part is to deliver the data in an efficient manner. For this, the major idea and enabler is to provide data links between sensing nodes and receiving stations for transmitted data. To satisfy different applications and related functionalities, it is important that these radios can timely transmit the data over larger distances while consuming less energy. The major competitors in this area are Low-Power Wide Area Networks (LPWAN) with their technology competitors: LoRa, NB-IoT and Sigfox. According to Fig. 24 LPWAN can satisfy long ranges wherein the data rate is sacrificed, just suitable for sensorial application. In these cases, sensor devices send several data packets containing only the sensed information.
Overview of technologies that can satisfy different usage scenarios ( Mekki et al., 2019 ).
When considering LPWANs, the competitive technologies are also orthogonal in terms of different application points of view. A good overview of these technologies is given in Fig. 25 and Fig. 26 , also by providing the costs for each of them. In addition, Figs. 25 and and26 26 give the technological comparison between each of them, so the deployers can understand which technology better fits which need. These mostly refer to which kind of infrastructure is required to match needs, what distance can be covered, what the overall system latency is when considering the number of nodes, etc.
Overview of performances and deployment costs for different LPWAN technologies ( Mekki et al., 2019 ).
Pros and cons for each of LPWAN competitors ( Mekki et al., 2019 ).
3.3. Power management
The basic mechanism that allows for the long lifetime of battery-operated devices (up to a couple of years) is to keep the device in low-power state during inactive periods. IoT devices, especially battery-operated ones, spend only a small fraction of time within active state, in which MCU performs sensor readings, and communicates data over wireless channels using a radio peripheral, while during inactive periods, the MCU along with other components is kept in deep-sleep state. Such a period between two active states, i.e. active - sleep - active is referred to as a duty cycle. Intuitively, to increase the lifetime of an IoT device, it is necessary to minimize its consumption during inactive periods. Logically, within inactive periods, it is necessary to place all active components into sleep. Some components, such as sensors, and radio modules, already come with libraries that support low-power consumption in sleep state (around 1uA per component or less). Using built-in functions, the MCU triggers external components to enter sleep once the sensor reading and radio transmission is done. On the other hand, the MCU also has to be kept in deep-sleep during the sleep period. However, to trigger the MCU waking from deep-sleep, some form of interrupt has to be sent to it. This is usually accomplished with some form of low-power timer. Depending on the MCU that is used in the implementation of an IoT device, there are many ways to accomplish this.
An MCU such as ATmega328P, found on Arduino boards, comes with a built-in Watchdog timer (WDT), with consumption up to couple of Ua, ( ATmega328P, 2020 ). Some external timers, like TPL5010 come with Watchdog functionalities, however, with nA scale consumption ( TPL5010, 2020 ). Unfortunately, the maximum time WDT can hold the MCU in low-power mode is around 8 s ( Tutorial - Atmega328p, 2020 ). One way to increase sleep time using WDT would require a loop that periodically triggers the MCU waking up every 8 s, after which the MCU immediately enters deep-sleep. Within deep-sleep period, the consumption of MCU and WDT is only a few uA. To increase sleep time for ATmega328P, an external RTC clock could be employed, such as a cheap and precise RS3231 RTC clock, with ±2 ppm stability and 1uA of consumption (Datasheet - RTC3231, 2020 ). Other MCUs, such as STM32 or SAMD21, already come with built-in RTC clocks that can be used to trigger an alarm for waking up from deep sleep (Libraries - Arduino low-power, 2020 ), ( STM32, 2020 ). All these components (MCU, sensor, RTC clock, radio peripheral, voltage regulators, capacitors, etc.), although in low-power mode, combined may consume tens to even couple of hundred of uA while being placed in deep-sleep. Moreover, it may happen that some boards equipped with components that adopt low-power modes have a hardware problem that prevent them from achieving low deep-sleep currents, such as found in MKRWAN1300 (Arduino LoRa with SAMD21) and ( MKRWAN1300, 2020 ).
To reduce even more consumption regarding all components, it is suggested to use an external timer component that will completely cut-off power for predefined periods. The TPL5110 is a low power timer where an alarm clock is regulated with resistors, allowing for the duration of sleep mode to be up to 2 h ( TPL5110, 2020 ). Within the sleep period, the TPL5110 simply cuts-off power from other components leaving overall consumption to be equal to the consumption of the timer only. Since the TPL5110 is low power by nature, the overall consumption falls to only 50 nA. The drawback of such a solution is that the MCU is no longer in deep-sleep but is instead powered off, which means that possible variables that were held in volatile memory during deep-sleep will not be available to the MCU when it wakes up. For this reason, it is suggested to use EEPROM or flash memory to write the variables before cutting off power from the MCU. A Tega328P may use built-in EEPROM, while STM32 or SAMD21 can use flash memory or RTC backup RAM ( Flash storage, 2020 ). The RS3231 RTC clock has an EEPROM that can be used for saving variables. The main drawback of EEPROM and flash memory is the limited number of writes (around 10,000), hence some external EEPROM or flash memory may be used with a larger number of writings, or either an external specialized chip like ATECC508A ( ATECC508A, 2020 ) that supports secure storage (of key for example) (ATEC). It must be noted that when the MCU wakes from deep-sleep, the code runs from where it left off, which usually requires a couple of mS. On the other hand, powering the MCU with an external timer such as TPL5110 requires a fresh restart of code, which in some scenarios may indicate running the bootloader. For ATmega328P, by default it may take up to 2 s for the bootloader to start ( Tutorial - Low-power nodes, 2020 ). Hence, to reduce consumption, it is suggested to either completely wipe out the bootloader or flash faster bootloader ( Bootloader, 2020 ). It must be mentioned that battery capacity, along with its input voltage may vary during sensor lifetime or could be larger than the operating voltage of some components. A good quality voltage regulator that may deliver enough current to a sensor device while consuming itself small current is required. For example, MCP1700 ( MCP1700, 2020 ) is a family of CMOS low dropout (LDO) voltage regulators that can deliver up to 250 mA of current while consuming only 1.6 μA, with input operating ranging from 2.3V to 6.0V, making it ideal for battery operated devices.
3.4. Microcontrollers for IoT: scouting and comparison
The Microcontroller (MCU hereafter) is the core of any Internet of Things (IoT) device and embedded system. Indeed, its role is to coordinate, according to a specific pre-programmed logic, all the peripherals of the IoT node thus providing sensing, actuation, and connectivity in an as low power mode as possible. In other words, the MCU sets the “smart-ability” of a certain object in relation with its cost, computational capability, power consumption, memory, communication interfaces and other features to accurately select during the design phase. It is worth highlighting that a “perfect” microcontroller does not exist, but just the most suitable one for the specific application. For this reason, the role of the designer in selecting the microcontroller for a specific IoT application is never simple. Some “universal” microcontroller key features are useful to drive the designer towards the right choice according to the requirements of the considered IoT application.
The proposed analysis aims at comparing some microcontrollers as potentially useful for the IoT by considering the following objective parameters.
- • Register Memory Bits : This parameter refers to the number of internal register bits and buffer. The higher the number of MCU bits, the higher the number of operations that the MCU itself can sustain. This parameter sets different families of Microcontrollers.
- • Maximum Clock Frequency: is the maximum frequency on the internal/external clock of the microcontroller. It is useful because it sets the number of operations of an MCU in a single time unit.
- • RAM : RAM is the volatile memory of an MCU which is useful for performing quick operations, actions or data buffering. The absence of powering resets this kind of memory
- • Flash Type : It is the static memory of an MCU that retains data in the absence of power. The quality of this memory in terms of writing operation figures and writing/reading speed determines a consistent part of the microcontroller cost.
- • Number and Type of GPIOs : GPIO is the acronym of a general-purpose input/output interface. It is referred to as the presence of pins that can be configured to act as the analog or digital input/output of the MCU. The higher the number of MCU GPIOs, the higher the number of external devices (sensors, actuators, transceivers) that can be controlled.
- • Serial Bus : Presence of an SPI/I2C bus for communication
- • Integrated Wireless Connectivity Interfaces: This key feature is useful in the IoT to wirelessly connect the MCU by using Wi-Fi, Ethernet, or BLE interfaces.
- • Power Consumption: Power Consumption is the most important aspect of IoT-oriented Microcontrollers. This parameter should be optimized by controlling the Active time and Sleeping time of the MCU according to the specific application.
- • Development board/Launchpad: The availability of a development board is helpful during the design phase to test the targeted IoT solution before realizing a prototype. Providing this board is an added value for MCUs.
- • Arduino IDE Programming Interface: Multi-brand MCUs implement an Arduino-compatible convergence programming language useful to simplify the programming operations and modular implementation of IoT applications.
- • Cost: IoT applications are often cost-sensitive. In many cases, functionalities could not be implemented to maintain a low-cost IoT system design. Generally, both MCUs and the presence of specific sensors determine the cost of the whole solution.
In addition to the above-mentioned parameters, the computational capability of a microcontroller can be evaluated by considering the presence of an on-board Operating System. If supported, this feature helps in managing complex IoT embedded applications where several peripherals must be managed. In this regard, three different typologies of microcontrollers can be summarized:
- • No-Operating system: The operating system is not present. In this case the microcontroller can be programmed in a “canonical” manner, by developing a code for low-level operations (Assembler of C are the main programming languages). A software-level connection cannot be implemented, however, the cost-effectiveness of these kinds of microcontrollers as well as reduced power consumption, make this MCU typology quite diffused.
- • RTOS : namely “Real-Time Operating System”. An RTOS Operating system enables a multi-task approach by introducing priority levels among the tasks running under the operating system. Moreover, this Operating System guarantees the correct timing of single events.
- • Linux/UNIX : This feature allows high-level programming in a way similar to a canonical computer. Open source software can be run on the MCU thus enabling connectivity and port management. Real-time and low-power operations are never guaranteed so that this kind of MCU is often not compatible with IoT applications, except for hi-level management IoT node systems.
Underneath, selected multi-brand MCUs will be compared by using the above-mentioned metrics in order to have a quick perspective useful in selecting the right MCU for a specific IoT application. After a quick overview of the microcontrollers based on manufacturer descriptions, which is useful to understand the different categories, a table summarizing their main features will be provided. Being low-power, “No-Operating system” devices will be considered in this comparison, Fig. 27 .
Comparison of microcontroller devices.
3.4.1. Texas instruments G series MSP430G2x13 and MSP430G2x53
The MSP430G2x13 and MSP430G2x53 series are ultra-low-power microcontrollers with built-in 16-bit timers, up to 24 I/O capacitive-touch enabled pins, a versatile analog comparator, and built-in communication capability using a universal serial communication interface. In addition, the MSP430G2x53 family members have a 10-bit analog-to-digital (A/D) converter. This is an entry-level microcontroller useful for general purpose low-power and low-cost IoT applications. The availability of a development board for the MSP43G2553 MCU, called “Launchpad”, makes the design easy for simple IoT sensing nodes.
3.4.2. Texas instruments F series MSP430F552x
The MSP430F5529, MSP430F5527, MSP430F5525, and MSP430F5521 microcontrollers have an integrated USB and PHY supporting USB 2.0, four 16-bit timers, a high-performance 12-bit analog-to-digital converter (ADC), two USCIs, a hardware multiplier, DMA, an RTC module with alarm capabilities, and 63 I/O pins. The MSP430F5528, MSP430F5526, MSP430F5524, and MSP430F5522 microcontrollers include these peripherals but have 47 I/O pins. This MCU family is compatible with low-power hi-performance IoT applications where hi-speed communication, port availability, and USB connectivity is required. Also in this case, the availability of a “Launchpad”, for the MSP430F5529 MCU makes the design easy for rather advanced and low-cost IoT smart nodes.
3.4.3. Texas instruments FR series MSP430FR572x and MSP430FR59xx
The TI MSP430FR572x and MSP430FR59xx families of ultra-low-power microcontrollers consist of multiple devices that feature an embedded FRAM nonvolatile memory, ultra-low-power 16-bit MSP430™ CPU, and different peripherals targeted for various applications. The architecture, FRAM, and peripherals, combined with seven low-power modes, are optimized to achieve extended battery life in portable and wireless sensing applications. FRAM is a new nonvolatile memory that combines the speed, flexibility, and endurance of SRAM with the stability and reliability of flash, all at lower total power consumption. Peripherals include a 10-bit ADC, a 16-channel comparator with voltage reference generation and hysteresis capabilities, three enhanced serial channels capable of I2C, SPI, or UART protocols, an internal DMA, a hardware multiplier, an RTC, five 16-bit timers, and digital I/Os.
3.4.4. Microchip PIC18F family PIC18F26K22
The PIC18 microcontroller family provides PICmicro® devices in 18-to 80-pin packages, that are both socket and software upwardly compatible to the PIC16 family. The PIC18 family includes all the popular peripherals, such as MSSP, ESCI, CCP, flexible 8- and 16-bit timers, PSP, 10-bit ADC, WDT, POR and CAN 2.0B Active for a maximum flexible solution. Most PIC18 devices will provide a FLASH program memory in sizes from 8 to 128 Kbytes and data RAM from 256 to 4 Kbytes; operating from 2.0 to 5.5 V, at speeds of DC to 40 MHz. Optimized for high-level languages like ANSI C, the PIC18 family offers a highly flexible solution for complex embedded applications.
3.4.5. Microchip PIC24F family PIC24F16KA102
The PIC24F is a cost-effective, low-power family of microcontrollers (MCUs) based on eXtreme Low Power (XLP) technology and 16-bit architecture. The flash memory ranges from 16 KB to 1 MB. The PIC24F family is a suitable solution for many space-constrained, low-power, cost-sensitive industrial, IoT and consumer applications.
3.4.6. STMicroelectronics STM32L0 family – STM32L053x8
The STM32L053x6/8 devices provide high power efficiency for a wide range of IoT applications. It is achieved with a large choice of internal and external clock sources, an internal voltage adaptation and several low-power modes. The STM32L053x6/8 devices offer several analog features, one 12-bit ADC with hardware oversampling, one DAC, two ultra-low-power comparators, several timers, one low-power timer (LPTIM), three general-purpose 16-bit timers and one basic timer, one RTC and SysTick which can be used as time bases. The MCU is provided with SPI, I2C, UART and USB 2.0 busses. This kind of MCU is studied for Ultra Low Power IoT applications and is provided with an effective development Arduino-compatible modular kit, called NUCLEO, allowing for easy interconnection with connectivity (BLE, Wi-Fi, Lo-Ra, etc) modules for IoT.
Taking into the account the above elaborated recent works, application scenarios and the enabling technology overview, following can be emphasized:
- - all kinds of services that are used to enable smart cities highly depend on the deployed hardware sensing infrastructure. Less infrastructure implies limited functionalities for given application scenarios. On contrary, many different application scenarios can be considered but this increases implementation and maintenance costs,
- - many different sensing techniques were proposed, and research community is intensively working to provide more reliable and cost-effective sensing technologies that can be easily implemented in IoT sensing nodes,
- - to enable different functionalities of the given application scenarios it is important to have the technology which can deliver sensed data on a greater distance, while preserving the energy in order to improve battery lifetime. For their products, many vendors specify 2–10 years of lifetime for their products, and it can be concluded that the battery lifetime depends on how frequently data is sensed and sent to the receiving station. Two-years span can certainly be considered as not enough, ten years’ horizon could be enough as by then, new technologies may arise and substitute currently implemented technologies. In any case, providing new technologies from any point of view: radios, MCUs, sensing techniques that can preserve battery lifetime is of crucial interest for both current and future IoT deployment,
- - currently available radios can fulfil today’s needs in terms of delivering data from remote areas in smart cities/villages. The NB-IoT, LoRa and Sigfox are overlapping in part, but can be considered as orthogonal for specific use-cases. Smart usage of given radios can further improve battery lifetime. However, it is always of the high interest to consume even less energy and provide larger communication distances and it provides the space for further analysis and technology improvements.
4. E-health – ambient assisted living systems
In recent years, the exploitation of new assisted living technologies has become necessary due to a rapidly aging society. In fact, it is estimated that 50% of the population in Europe will be over 60 years old in 2040, while in the USA it is estimated that one in every six citizens will be over 65 years old in 2020 ( Corchado et al., 2008 ). In addition, in 75-year-olds, the risk of Mild Cognitive Impairment (MCI) and frailty increases and people over 85 years of age usually require continuous monitoring. This suggests that taking care of elderly people is a challenging and very important issue. People with limited mobility are increasingly looking for innovative services that can help their daily activities. Ambient Assisted Living (AAL) encompasses technological systems to support people in their daily routine to allow an independent and safe lifestyle as long as possible. AAL (or simply assisted living) solutions can provide a positive influence on health and quality of life, especially with the elderly. An AAL approach is the way to guarantee better life conditions for the aged and people with limited mobility, chronic diseases and in recovery status with the development of innovative technologies and services.
Modern assistive technologies constitute a wide range of technological solutions aimed at improving the well-being of the elderly, Fig. 28 . These technologies are used for personalized medicine, smart health, health tracking, telehealth, health-as-a-service (HaaS), smart drugs and multiple other applications ( Maskeliunas et al., 2019 ).
IoT technology applications for AAL domain, ( Maskeliunas et al., 2019 ).
AAL technologies can also provide more safety for the elderly, offering emergency response mechanisms ( Lin et al., 2013 ), fall detection solutions ( Kong et al., 2018 ), and video surveillance systems ( Meinel et al., 2014 ). Other AAL technologies were designed in order to provide support in daily life, by monitoring the activities of daily living (ADL) ( Reena and Parameswari, 2019 ), by generating reminders ( Uribe et al., 2011 ), as well as by allowing older adults to connect with their families and medical staff. The recent advancements in mobile and wearable sensors helped the vision of AAL to become a reality. All novel mobile devices are equipped with different sensors such as accelerometers, gyroscopes, a Global Positioning System (GPS) and so on, which can be used for detecting user mobility. In the same way, recent advances in electronic and microelectromechanical sensor (MEMS) technology promise a new era of sensor technology for health ( Vohnout et al., 2010 ). Researchers have already developed noninvasive sensors in the form of patches, small holter-type devices, wearable devices, and smart garments to monitor health signals. For example, blood glucose, blood pressure, and cardiac activity can be measured through wearable sensors using techniques such as infrared or optical sensing. User localization is another key concept in AAL systems because it allows tracking, monitoring, and providing fine-grained location-based services for the elderly. While GPS is the most widespread and reliable technology to deal with outdoor localization issues, in indoor scenarios it has a limited usage due to its limited accuracy due to the impact of obstacles on the received signals. The number of alternative indoor positioning systems have been proposed in the literature ( Mainetti et al., 2014 ) that can be exploited in order to support AAL systems. Among all technologies, Bluetooth (BT) technology represents a valid alternative for indoor localization ( Yapeng et al., 2013 ) or specifically in museums ( Alletto et al., 2015 ). It is able to guarantee a low cost since it is integrated in most of daily used devices such as tablets and smart phones. The spread of the emerging Bluetooth Low Energy (BLE) technology makes the BT also energy-efficient, which is a key requirement in many indoor applications. An interesting investigation regarding the state-of-the-art and adaptive AAL platforms for older adult assistance was provided in ( Duarte et al., 2018 ). The authors present an overview of AAL platforms, development patterns, and main challenges in this domain.
In recent years, a large number of solutions have been proposed in the literature in order to create smart environments and applications to support elderly people. The main purpose is to provide a level of independence at home and improve elderly quality of life. In ( Dobre et al., 2018 ), an architecture which constitutes the base for the development of an integrated Internet of Things (IoT) platform to deliver non-intrusive monitoring and support for older adults to augment professional healthcare giving is presented, Fig. 29 . The proposed architecture integrates proven open-data analytics technology with innovative user-driven IoT devices to assist caregivers and provide smart care for older adults at out-patients clinics and outdoors.
Proposed modular architecture, ( Dobre et al., 2018 ).
A solution for monitoring patients with specific diseases such as diabetes using mobile devices is discussed in ( Villarreal et al., 2014 ). The proposed system provides continuous monitoring and real time services, collecting the information from healthcare and monitoring devices located in the home environment which are connected to BT mobile devices. The sensor data are transmitted to a central database for medical server evaluation and monitoring via 3G and Wi-Fi networks. An ad hoc application, installed on a mobile phone, allows the remote control of a patient’s health status whilst the patient can receive any notifications from the health care professionals via the application running on her/his mobile phone, Fig. 30 .
Proposed system for continuous monitoring and real time services, ( Villarreal et al., 2014 ).
The work ( Villarrubia et al., 2014 ) proposes a monitoring and tracking system for people with medical problems whose system architecture is shown in Fig. 31 . The solution includes a system for performing biomedical measurements, locomotor activity monitoring through accelerometers and Wi-Fi networks. The interactive approach involves the user, through a smart TV. The locomotor activity of the elderly is deduced through the analysis of Received Signal Strength Indication (RSSI) measurements, i.e. through an algorithm, the received signal power from different access points located in the house is determined. Mobile accelerometers are used to analyze the movement of users and detect steps. Single board computers, such as Raspberry Pi, are used to collect data coming from the different sensors wirelessly connected to obtain real-time context-aware information such as gas, temperature, fire, etc. or to get information from biomedical sensors such as, oxygen meter, blood pressure, ECG, accelerometer, etc. The Raspberry Pi can be connected to a TV to transmit warnings or notifications coming from health care professionals.
Virtual organization of system, ( Villarrubia et al., 2014 ).
The work ( Mainetti et al., 2016 ) proposes an AAL system for elderly assistance applications able to provide both outdoor and indoor localization by using a single wearable device. A prototypal device has been developed exploiting GPS technology for outdoor localization and BLE technology for indoor localization. The proposed system is also able to collect all information coming from heterogeneous sensors and forward it towards a remote service that is able to trigger events (e.g., push notifications to families or caregivers and notifications to the same indoor environment that will change its status). In an enriched work ( Mainetti et al., 2017 ), presents an architecture that exploits IoT technologies to capture personal data for automatically recognizing changes in the behaviour of elderly people in an unobtrusive, low-cost and low-power manner, Fig. 32 . The system allows performing a behavioral analysis of elderly people to prevent the occurrence of MCI and frailty problems.
Overall logical architecture ( Mainetti et al., 2017 ).
Based on the recent analysed research works on the use of IoT technologies in the e-health and for the creation of AAL systems, it is possible to draw the following general observations:
- - an extensive research is aimed at creating AAL systems intended primarily for the elderly or for people with physical or mental diseases,
- - current challenges deal with the use of IoT technologies in order to capture the habits of the people monitored both in indoor and outdoor environments for behavioral analysis purposes. The behavioral analysis can be useful for monitoring people, scheduling interventions and providing notifications directly to the user,
- - increasing efforts are needed in order to unobtrusively capture habits by favoring the use of wearable devices.
5. IoT technologies in Transportation and Low Carbon Products
The issue of security and traceability of goods is increasingly important in the logistics sector, with repercussions in terms of supply chain management and goods transport. In this case, information technologies and in particular the IoT can offer valuable support, increasing the degree of visibility and control over the entire supply chain. Transportation is a good example of how IoT technologies can bring value. In fact, this sector needs systems that on the one hand allow for the planning, management and optimization of flows (both along the supply chain and within complex logistics hubs such as intermodal ones) and, on the other hand, allow for the traceability of goods (products or containers) in real time along the entire supply chain. A further requirement concerns the check of goods integrity. In this context, it is clear how IoT technologies can contribute to the remote monitoring of flows and assets, providing a series of information useful for their management and optimization. This is possible through identification (e.g., via RFID or barcode), location (e.g., via GPS), monitoring of parameters and status variables of the assets (e.g., via sensors) and their transmission (e.g., via Wi-Fi or GSM/GPRS network).
The advent of IoT technologies allows to organize, automate and control processes remotely and from any device connected to the Internet. By definition, an efficient supply chain is responsible for delivering the goods, from the manufacturer to the end user, at the agreed time and under the specified conditions. Through the use of IoT technologies, it is possible to track the entire process in real time, promoting speed and efficiency in automated processes, reducing time and personnel costs. IoT technologies such as sensors, embedded and mobile devices, and cloud storage systems allow for the connection of “things” (warehouses, vehicles or goods) to the Internet so that the manufacturer, the logistics service provider and even the end user can thoroughly know at any time the status of products, their location and estimated delivery time.
Logistics can benefit from the use of IoT technologies in all the following sectors:
- • efficient inventory and warehouse management
- • automation of internal business processes
- • fast and efficient delivery of products (e.g., route planning)
- • conservation and quality of transported goods (e.g., monitoring of cold chain)
- • location, monitoring and tracking of vehicle fleets
- • interactive communication between vehicles and manufacturers/distributors of goods
- • certification of both deliveries and transport phases
The basic principles of logistics always remain valid: transfer the right product, in the right quantity and condition, at the right time and right price, in the right place and to the right customer. As carrying out each of these tasks has become much more complicated in an increasingly globalized and interconnected world, the need for innovative solutions to achieve these objectives also increases. As mentioned above, the IoT is revolutionizing the logistics sector, offering many advantages and opportunities. Supply chain monitoring, vehicle tracking, inventory management, secure transport and process automation are the cornerstones of IoT applications as well as the main elements of interconnected logistics systems.
In the logistics sector, the IoT allows creating smart location management systems, which allow companies to easily monitor driver activities, vehicle location and delivery status, ( Brincat et al., 2019 ). This solution is indispensable in the planning of deliveries and the organization of timetables and reservations. It is possible to detect any changes in real time and this is precisely the reason behind the success of the IoT: the ability to improve the management of good movement and therefore streamline business processes. Inventory and warehouse management is another important element of the connected logistics ecosystem. The positioning of small sensors allows companies to easily track items in warehouses, monitor their status, position and create a smart control system. In fact, with the help of IoT technology, employees will be able to successfully prevent any loss, ensure the safe storage of goods and efficiently locate the product needed. Even the minimization of human error becomes possible thanks to the IoT. In this scenario ( Wang et al., 2015 ), proposes a layered architecture for the realization of an automation enterprise asset management system using IoT and RFID technologies, Fig. 33 .
Layered architecture of proposed automation enterprise asset management system ( Wang et al., 2015 ).
The sustainable and IoT supported business model was discussed in ( Gao and Li, 2020 ) for the case of the bike-sharing services. Novel framework was developed that links sustainable indicators as well as social aspects of the business concept. The case studies for dockless bike-sharing services were discussed and presented for China and UK. Practical findings extended knowledge needed for improvement of the sharing economy to achieve sustainably goals through IoT enabled support. The work ( Zhang et al., 2016 ) proposes an inventory management system for a warehousing company. The system adopts the concept of IoT using RFID technology to track the material and provide messages or warnings when incorrect behaviors are detected. In particular, it integrates RFID technology and a self-Adaptive distributed decision support model for inbound and outbound actives, inventory location suggestions and incident handling. In ( Guptha et al., 2018 ), the authors design an IoT architecture for order picking processes in a warehouse that allows the inventory real time tracking and visibility into the reduction of warehouse operation costs, improved safety and reduced theft. IoT and RFID technologies are again exploited in ( Valente et al., 2017 ) to improve productivity in the value chain of a steel mill. In this work, an existing RFID solution architecture based on the reference EPCGlobal/GS1 framework was modified in order to be extended to the IoT domain, Fig. 34 .
RFID/IoT solution architecture for steel mill (Valente et al., 2017).
The internet-connected devices collect large amounts of data which can be transmitted to a central system for further analysis. In this context, the integration between IoT and predictive analysis systems can help companies to create effective business development strategies, improve decision-making and manage risks. In the logistics sector, this integration finds application to plan routes and deliveries as well as identify various defects before something goes wrong. An integrated framework to track and monitor shipped packages, Fig. 29 was proposed in ( Proto et al., 2020 ). Framework relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict the potential breaks of goods in the packages ( Fig. 35 ).
Framework architecture, ( Proto et al., 2020 ).
Ensuring product quality and integrity is an interesting challenge that in recent years has led to the creation of smart systems that integrate IoT solutions and block chain technology. The Blockchain technology associated with IoT sensors could allow the creation of a temporal “stamp” inside which a series of information is kept such as product delivery date, product characteristics and status, and origin of product. By positioning the sensors, for example, it is possible to monitor parameters such as product temperature and humidity, vehicle position and phases of the transport process and save this data in the block chain. Block chain infrastructure can also revolutionize company logistics in the field of document management (i.e., invoices, transport documents, etc.), traceability of goods (origin of products, monitoring of vehicle fleets, etc.), and play a substantial role in fighting counterfeiting. Imeri and Khadraoui (2018) showed a conceptual approach to the security and traceability of shared information in the process of dangerous goods transportation using block chain technology based on smart contracts. IoT and block chain technologies are exploited in ( Arumugam et al., 2018 ) where a smart logistics solution encapsulating smart contracts, logistics planner and condition monitoring of the assets in the supply chain management area is presented, Fig. 36 . Moreover, a prototype of the proposed solution is implemented.
High-level architecture of proposed solution, ( Arumugam et al., 2018 ).
The block chain-IoT-based food traceability system (BIFTS) to integrate the novel deployment of block chain, IoT technology, and fuzzy logic into a total traceability shelf life management system for the managing of perishable food, Fig. 37 was proposed in ( Tsang et al., 2019 ). Challenges in the adoption of the proposed framework in the food industry are analysed and future research planned to improve the proposed work.
Modular framework of BIFTS, ( Tsang et al., 2019 ).
Taking into account above discusses recent research findings further main findings could be highlighted:
- - The studies analysed previously show how the hardware and software technologies enabling the Internet of Things are leading to a digital transformation process that aims at an intelligent and advanced management of the entire logistics and transportation system.
- - The main scientific challenges in this field aim to use sensors in order to monitor the status of the goods transported, to ensure traceability and above all to safely and reliably collect telemetry data and offer them to Artificial Intelligence modules for advanced processing.
- - Furthermore, recently the interest has focused on the next generation of blockchain systems (the so-called blockchain 3.0) which aims to apply the benefits of the classic blockchain in typical scenarios of the Internet of Things, such as logistic and transportations.
6. Concluding remarks and future directions in the field
This review paper discussed and presented latest research findings that were included within the JCELPRO VSI SpliTech2019 and dedicated to the 4th International Conference on Smart and Sustainable Technologies (SpliTech 2019). The contributions as well as herein presented knowledge is summarized and discussed in upcoming sections.
The Intense digitalization in recent years has allowed for different technological possibilities that have already gradually been changing the main economic sectors and societies in general. Digitalization in different economic sectors enabled various possibilities for advancements and for a more efficient utilization of limited resources, systems or processes. The main driver for an efficient digitalization in various sectors is information technology, i.e., IoT supported smart technologies. In the previous sense, the energy sector is one of the key sectors where ‘‘energy digitalization’’ has already been rapidly developing in various energy related fields. Currently, one of the most progressing implementation areas of IoT technologies is related to the energy sector. The developing solutions are focused on smart homes, i.e. advanced automatization of home energy systems, development of smart and adaptive micro-grids, or advancements in efficient demand-side management of power systems. A circular economy concept has also been intensively worked on where various concepts have been investigated, which can support smart waste management and help bridge one of the main challenges in society. Recently, different concepts have been tested where IoT technologies could be used for environmental protection, primarily for the monitoring of air quality, which is a big potential in that sense.
Healthcare systems can also be significantly improved with the application of IoT devices, i.e. via the E-health concept. An improved quality of services and patient safety could be enabled with an advanced IoT supported monitoring system. The prediction of life threatening states could be efficiently detected with a better treatment of patients, such as timely therapy decisions and qualitative rehabilitation. In general, large healthcare systems could also benefit from IoT, both in efficiency and from a cost aspect, which is important for hospitals. The current pandemic state with COVID-19 allowed for the consideration of different IoT applications or devices that could help in efficiently monitoring and controlling the pandemic, which proves the added value of IoT products.
The transportation sector is currently in gradual transition where a mix of transportation vehicle technologies is expected in upcoming decades with the involvement of electric vehicles primarily along with hybrid or hydrogen based vehicles. The main advancements of IoT in transportation are the support of the smart car concept where different vehicle operating parameters can be monitored in an efficient manner. The main advantage is early detection of severe failures, then regular maintenance, improved fuelling and finally improvement of safety and driving experience in general. The most challenging IoT application area is in the case of autonomous vehicles, where safety is the main goal and in that sense, significant research advancements are expected to occur in the near future.
The smart city concept is the most progressing IoT application area since cities have been vastly populated, which causes severe infrastructural issues. The main benefit of IoT technologies in the smart city concept is to bridge severe infrastructural challenges in highly populated cities. The improvement of life quality in cities is also expected thanks to the efficiency improvement of various convectional services in cities. The early detection of various and common daily problems in cities could be efficiently solved with IoT as with transportation issues, energy and water shortage supplies, security issues, etc. The biggest challenge in the smart city concept is directed to the efficient networking and operation of different sensing technologies, which must be followed with the proper education of the population.
Each technology that is rapidly progressing has got specific potential drawbacks that need to be carefully analysed and addressed. Since IoT devices are measured in billions, and with large potential impacts on the population, specific challenges need to be addressed, which were detected based on the herein conducted review. The main goal is to secure a sustainable and balanced development of IoT technologies. Therefore, further issues are briefly discussed below and should be carefully considered during the further development of IoT technologies:
- - the rapid development of IoT technologies causes fast consumption of raw materials to produce different electronic devices where unfortunately some of raw materials are already rare or becoming,
- - electronic devices are becoming more economically acceptable where a potentially large population would be affected. High production volumes are expected which can finally cause a rebound effect and a more rapid unwanted utilization of already limited resources,
- - the sustainability aspect and long-term effects of IoT technologies are not clear and insufficiently investigated. A noticeable amount of energy would be needed to operate IoT devices and the electronic industry is leaving different unfavourable environmental footprints that also need to be carefully investigated,
- - electronic waste will become one of the major issues caused with the planned rise of IoT products. Recycling rates must be improved and better e-waste management should be secured,
- - IoT technologies can cause social impacts in specific industrial branches or businesses since working labour could be reduced and direct social contacts have also been reduced. In that sense, the application of IoT technologies should be carefully considered taking the raised issues into account,
- - significant advancements in both specific electronic components as well as user-friendly software solutions are required,
- - further development in sensing technologies and advanced data acquisition systems is also required,
- - the minimization of energy consumption in IoT devices is a crucial target, i.e. reduction of energy supply.
From the herein addressed recent research findings within the VSI SpliTech 2019, it is obvious that developments in various IoT application sectors are promising but further advancements are expected and that are mainly focused on maximizing the efficiency of specific IoT supported processes or technologies, minimizing resource utilization (raw materials and energy) and environmental footprints. IoT technologies are an opportunity for humanity and can bring important as well as useful benefits to the population. The authors contributions within the JCLEPRO VSI SpliTech2019 provided quality discussion and presentation of the latest advancements in the field, and most important, they contributed to the better understanding of IoT application areas, technological possibilities, but also potential drawbacks and issues that should be carefully monitored in future terms. The crucial and important aspects are linked with sustainability where the rapid developments in IoT technologies must be carefully monitored from a resource and environmental point of view to ensure balanced and sustainable development of IoT products. Herein presented knowledge and published works in the Journal of Cleaner Production are serving as important foundations for researchers dealing with this challenging and dynamic research field.
CRediT authorship contribution statement
Sandro Nižetić: Conceptualization, Methodology, Supervision. Petar Šolić: Conceptualization, Methodology, Supervision. Diego López-de-Ipiña González-de-Artaza: Supervision. Luigi Patrono: Conceptualization, Methodology, Supervision.
Declaration of competing interest
We wish to confirm that there are no known conflicts of interest associated with this publication in Journal of Cleaner Production ( Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future ) and there has been no significant financial support for this work that could have influenced its outcome.
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.
We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from ( [email protected] ).
This work has been supported in part by Croatian Science Foundation under the project “Internet of Things: Research and Applications”, UIP-2017-05-4206, Croatia.
Handling editor: Cecilia Maria Villas Bôas de Almeida
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