Insights on the internet of things: past, present, and future directions

Abstract

INTRODUCTION Kevin Ashton, in a 1999 presentation to Proctor and Gamble, coined the phrase "internet of things" (IoT) to describe the company's plans to employ radio-frequency identification (RFID) tags across its supply chain [1]. The word 'thing" refers to any natural or man-made object that can be given an internet protocol (IP) address and can send and receive data over a network, such as a person who has an implanted heart monitor, an animal on a farm that has a biochip transponder, a vehicle that has built-in sensors to alert the driver when the tire pressure is low, or any other similar thing [18]-[36]. Some of the most common IoT benefits enable businesses to: monitor their whole business operations; enhance customer experience (CX); save time and money; raise staff efficiency; integrate and adapt business models; make better business decisions; and generate more cash IoT forces us to reconsider our business practices and offers us with the tools we need to improve our corporate strategy. A home automation company could use the IoT to monitor and control the building's mechanical and electrical systems.

Headnote

ABSTRACT

The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

Keywords:

Consumer Industrial Internet of things IoT architecture IoT ecosystem Machine learning

1. INTRODUCTION

Kevin Ashton, in a 1999 presentation to Proctor and Gamble, coined the phrase "internet of things" (IoT) to describe the company's plans to employ radio-frequency identification (RFID) tags across its supply chain [1]. This would give computers their own eyes, ears, and noses so they could observe the environment for themselves. It expanded upon prior concepts, most notably Mark Weiser's vision for ubiquitous computing, laid out in his 1991 article for Scientific American (The Computer for the 21st Century), in which he envisioned a future world populated by a vast network of interconnected computers that would become so intrinsic to daily life that they would be invisible to the naked eye [2]. However, the IoT did not really begin to take off on a worldwide scale until Gartner included it on its list of new emerging technologies in the year 2011. IoT devices accounted for more than 11.3 billion (or 52 percent of the 21.7 billion active connected devices in use globally) as of 2021, and it is anticipated that the number of linked IoT devices would rise by 16 percent to 13.1 billion in 2022 as shown in Figure 1 [3]. This suggests that there are more IoT devices than there are devices that are not connected to the internet all across the world.

The IoT is an interconnected network of computing devices, mechanical and digital equipment, objects, animals, or humans assigned unique identifiers (UfDs) [4], [5] and the capacity to transfer data via a network without the need for human-to-computer or human-to-human interaction \6]-\ 17]. The word 'thing" refers to any natural or man-made object that can be given an internet protocol (IP) address and can send and receive data over a network, such as a person who has an implanted heart monitor, an animal on a farm that has a biochip transponder, a vehicle that has built-in sensors to alert the driver when the tire pressure is low, or any other similar thing [18]-[36]. IoT is being utilized more and more frequently by organizations across a wide variety of sectors to improve operational efficiencies, gain a deeper understanding of their clientele in order to deliver superior service, enhance decision-making capabilities, and add value to their operations [20], [25], [28], [36].

Figure 2 depicts an IoT ecosystem, which is made up of web-enabled smart devices that collect, send, and act on data collected from their surroundings using embedded systems such as processors, sensors, and communication equipment [37]-[65]. IoT devices exchange sensor data through a link to an IoT gateway or another edge device, after which the data is either transferred to the cloud for processing or examined locally [59]. These electronic gizmos will periodically communicate with other connected devices and will take action based on the information they receive from one another. The majority of the work is done without human intervention, but individuals must engage with the devices to set them up, offer instructions, or see the results. The IoT apps that are installed have a significant influence on the connection, networking, and communication protocols that these web-enabled devices utilize. Machine learning and artificial intelligence (AI) may also be used in IoT to simplify and dynamically acquire data [66]-[133].

The IoT enables individuals to live and work more effectively, as well as having total control over their life. IoT is critical for businesses, in addition to delivering smart gadgets for home automation [7], [58], [66], [73], [78], [122]. IoT provides organizations with a real-time perspective of how their systems are doing, providing insights into machine performance, supply chain, and logistical operations [71], [97]. The IoT allows businesses to automate operations and save labor costs. It also reduces waste and enhances service delivery by reducing the cost of making and transporting goods and by providing customers with more transaction-related information [68], [73], [90], [116], [134]. Due to the fact that more businesses are realizing the value of connected devices for their ability to compete, IoT has grown to become one of the most important technologies in modern life.

2. APPLICATIONS AND BENEFITS

The IoT has various real-world applications, spanning from consumer, commercial, manufacturing as well as industrial IoT (IIoT) [59], [135]-[148]. IoT applications may be found in a variety of industries, including automotive, telecommunications, and energy [20], [122], [139], [140], [149]. Smart homes furnished with smart appliances, smart thermostats, and linked lighting, heating, and electrical devices, for example, may be handled remotely via computers and smartphones in the consumer market [150]-[155]. Wearable devices with sensors and software may gather and analyze user data, transmitting signals about the users to other technologies in order to make their lives simpler and more pleasant. Wearable tech is also used in public safety. For example, it can help first responders get to an emergency faster by showing them the best way to get there, or it can keep track of construction workers' or firefighters' vital signs in places that could be dangerous [120], [156]. In healthcare, IoT has several advantages, including the capacity to more closely monitor patients via data analysis. IoT systems are often employed in hospitals to control the supply of drugs and medical equipment [48], [55], [123], [125], [149]. By using sensors to determine the number of people present in a space, smart buildings, for instance, may reduce energy use. The temperature may be controlled automatically, for example by turning on the air conditioner if sensors detect that a conference room is full or by decreasing the temperature if everyone has left the office [47], [105], [157]-[161]. Linked sensors may be utilized by IoT-based smart farming systems to monitor the light, temperature, humidity, and soil moisture of agricultural areas. IoT may also be utilized to automate irrigation systems [4], [54], [162]-[165]. IoT sensors and installations such as smart lighting and smart meters might help decrease traffic, save energy, monitor and address environmental concerns, and make the city cleaner in a smart city [47], [80].

We can benefit from IoT in a number of ways. Some benefits are industry-specific, while others are applicable across several industries. Some of the most common IoT benefits enable businesses to: monitor their whole business operations; enhance customer experience (CX); save time and money; raise staff efficiency; integrate and adapt business models; make better business decisions; and generate more cash IoT forces us to reconsider our business practices and offers us with the tools we need to improve our corporate strategy. IoT is especially common in industrial, transportation, and utility companies that use sensors and other IoT devices. It has, however, discovered application cases for firms in the home automation, agricultural and infrastructure sectors, driving some to digital evolution [66], [91], [98], [100], [166]-[170].

IoT can help farmers because it can make their jobs easier. Sensors can measure things like rainfall, temperature, humidity, and the amount of nutrients in the soil, among other things, to help automate fanning. IoT may also help with being able to keep an eye on what's going on around infrastructure. For example, sensors could be used to keep an eye on what's going on inside buildings, bridges, and other infrastructure. This has benefits like saving money, saving time, changing the way work is done to improve quality of life, and getting rid of paper work. A home automation company could use the IoT to monitor and control the building's mechanical and electrical systems. On a larger scale, smart cities may assist inhabitants in reducing trash and energy use. The Internet of Things has an impact on every area, including healthcare, banking, retail, and manufacturing [150], [157], [162]-[165], [171]-[178].

3. ARCHITECTURE STAGES

The IoT refers to the widespread networking of various items, including consumer goods such as watches, wearable technologies, remote controls, tablets, household appliances, sensors, and more, with people who use them [179]-[183]. The solution is intended to collect data from devices and deliver it to data centers and servers for further analysis, which drives actions and automation. The IoT architecture is critical in directing data in the appropriate direction, establishing the format to use, and choosing what action to take. It is a network system made up of many components including sensors, actuators, cloud services, protocols, and IoT architectural layers [184]-[192]. Administrators may analyze, monitor, and maintain system consistency using several levels. For maximum effect, the system design strategy is thoroughly linked with the existing infrastructure systems. Organizations with strong IoT architecture have a higher chance of enhancing business processes and achieving better results. Such companies are known to maintain an IoT system architecture that is tailored to individual IoT initiatives, as well as other general-purpose IoT architectural formats. In addition, the system features IoT architectural layers that aid in system consistency monitoring. In reality, the layers must be in place long before the IoT architectural conceptualization process starts. A typical IoT architecture would consist of three layers: a) perception is the client layer that gathers data;

b) network is the server-side operator that links devices to smart objects, servers, and network devices; and

c) application is the final application that connects the operator and the client. These layers make certain that the IoT architecture is completely functional, scalable, available, and maintained [156], [193]-[203].

After that, we proceed to the four stages of the IoT architectural layout: sensors and actuators, gateways and data acquisition systems, edge IT data processing, and datacenter and cloud. A more in-depth examination of each of the four phases of IoT architecture: 1) Sensors and actuators [70], [84], [95], [102], [204]-[208]

Sensors and actuators are two types of linked devices that monitor and control physical processes. Sensors record process status data as well as external factors such as humidity, temperature, fluid movement in a pipeline, fluid level in a tank, and much more. Some condition data need an instantaneous reaction from the actuator in order to carry out real-time corrective activities. Adjusting the liquid flow rate to maintain a constant level is one example. It is critical to keep the latency between sensors and data processing as short as possible in order to trigger the actuator's action. To prevent delays in data relay to the server, its analysis, and the final signal to operate the 'thing,' data processing is performed near to the monitor and control system.

2) Gateways and data acquisition systems [43], [59], [66], [71], [113], [140], [158], [209]-[219]

A data acquisition system (DAS) collects the data given by the sensors and converts it to a digital-analog format. The DAS combines and prepares this data before sending it to the next level of processing through Internet gateways such as wireless wide area network (WANs), such as cellular or wireless fidelity (Wi-Fi), or wired WANs. In the case of industrial and manufacturing settings, the data at this level might be massive, with thousands of sensors collecting it at the same time. Before transmission, data must be filtered and compressed to an ideal size.

3) Edge IT data processing [67], [94], [100], [109], [122], [133], [140], [168]-[170], [216], [220]-[223] Before reaching the cloud center, the digitized and aggregated IoT data is processed further. Edge devices do sophisticated analytics and preprocessing, which may include machine learning and visual representation. Machine learning helps to provide ever-improving input into the system and further improve the process without having to wait for instructions from the cloud data center. Such processing is often performed in a device adjacent to the sensor, such as an on-site wire closet. This step then allows data to be collected at local sensors and sent to faraway sites for analysis and processing.

4) Data center and cloud [88], [152], [224]-[231]

In this last stage, data centers do extensive processing with the assistance of high-end programs built and managed by expert analytics specialists. IT systems that are powerful assess, handle, and store data in the cloud or corporate data centers. Multiple site sensors are combined here to provide a more comprehensive view of the complete IoT system and its deliverable actions. When activities are dispersed over many regions, these cloud data centers monitor and detect critical trends, patterns, or anomalies to aid in the optimization of operations. This is the stage at which a firm or industry-specific application conducts an in-depth analysis with unique or bespoke business rules in mind to decide the next step. Incoming data may indicate that device settings should be changed or recommend other remedial steps to enhance operations. It is a component of a continuous development loop that also saves data for future study.

4. OBSTACLES AND CHALLENGES

The expected exponential rise of smart devices, as well as the merging of low-cost infrastructure, connectivity, and data, will propel IoT from lofty idea to reality. Reduced device prices, extensive and pervasive connection, and a growing emphasis on operational efficiency and productivity are driving the mainstream use of IoT solutions. The IoT offers a concept in which the Internet expands into the physical world, including ordinary things. Physical objects are no longer cut off from the virtual world; they may now be operated remotely and serve as physical access points to Internet services. This progress is creating enormous potential for the economy as well as for people [20], [63], [78], [115], [116], [134], [146], [188], [189], [196], [197], [232]-[234].

The IoT concept is based on the premise that the recent continuous developments in microelectronics, communications, and information technology will continue in the near future. Processors, communications modules, and other electronic components are progressively being incorporated into ordinary things owing to their shrinking size, consistently lowering costs, and decreasing energy usage, "smart" things are important in the IoT concept because embedded communication and information technology has the ability to transform the usefulness of these products. They can sense their surroundings via sensors, and they can talk with one another, access Internet services, and interact with people thanks to built-in networking capabilities. "Digitally upgrading" traditional items in this manner improves their physical function by incorporating the capabilities of digital objects, resulting in significant additional value. Sewing machines, exercise cycles, electronic toothbrushes, washing machines, energy meters, and photocopiers are already being "computerized" and outfitted with network ports as forerunners of this evolution [235]-[248].

Despite IoT's enormous promise, enterprises must overcome various obstacles and challenges that are impeding IoT development. It is fraught with danger and, without a doubt, offers a massive technological and societal challenge. To learn about IoT and master it in the long run, businesses will need to work closely with mature providers to overcome significant obstacles such as: 1) A lack of standards and interoperable technologies [249]-[264]

Interoperability refers to the capacity to exchange data between various IoT devices and platforms. This information interchange is independent of the software and hardware in use. The issue of interoperability arises owing to the diverse nature of various technologies and solutions employed in IoT development. Technical, semantic, syntactic, and organizational interoperability are the four layers of interoperability. IoT systems provide a variety of features to increase interoperability, which facilitates communication between diverse items in a heterogeneous environment. Furthermore, multiple IoT platforms may be combined based on their characteristics to give a variety of solutions to IoT consumers. Given the importance of interoperability, researchers authorized many methods known as interoperability handling methodologies. These solutions might be based on adapters/gateways, virtual networks/overlays, service-oriented architectures, and so on. Although interoperability management mechanisms alleviate some of the load on IoT systems, there are still certain difficulties with interoperability that might be the subject of future research.

2) Data and information management issues [265]-[268]

Traditional infrastructures struggle to route, capture, analyze, and apply the insights created by massive amounts of IoT data in timely and appropriate ways. The sheer volume of data gathered will need advanced algorithms capable of sifting, analyzing, and extracting value from the data. As more devices are introduced to the market, more data silos emerge, resulting in a complicated network of links between isolated data sources. Organizations will find it considerably more difficult to eliminate data silos if uniform standards and protocols do not exist.

3) Privacy and security concerns [20], [41], [196], [221]

One of the most essential and difficult concerns in IoT is privacy and security owing to a variety of threats such as cyber-attacks, dangers, and vulnerabilities. Insufficient authorization and authentication, insecure software, firmware, web interface, and inadequate transport layer encryption are the challenges that give birth to device level privacy. Security and privacy concerns are critical factors in instilling trust in IoT devices in a variety of ways. To avoid security risks and assaults, security methods must be included in every tier of the IoT architecture. To secure the security and privacy of IoT-based systems, several protocols have been designed and efficiently applied on every tier of the communication channel. Datagram transport layer security (DTLS) is one of the cryptographic protocols used to provide security solutions in various IoT systems. It is implemented between the transport and application levels. However, various solutions are required for specific IoT applications to ensure the security of communication between IoT devices. Furthermore, communication inside the IoT system via wireless technology makes it more exposed to security vulnerabilities. As a result, certain approaches should be used to identify harmful behaviors as well as for self-healing or recovery. On the other hand, privacy is a crucial consideration that allows consumers to feel secure and comfortable when utilizing IoT solutions. As a result, maintaining authorization and authentication via a secure network is essential to facilitate communication between trusted parties. Another difficulty is that various items interacting inside the IoT system have varying privacy rules. As a result, before transferring data, each device in an IoT system should be able to validate the privacy policies of other objects.

4) Organizational inability to manage IoT complexities [269]-[283]

While the IoT provides immense value, using it will need a whole new level of systems and skills capable of harnessing the ecosystem and unlocking value for enterprises. For example, making sense of the deluge of data created by sensors every millisecond would need excellent data management, storage, and analytics skills. Similarly, policymakers will need to handle data security and privacy issues. Organizations will also need to build abilities to anticipate probable component failures and replacements via preventative service and maintenance methods to guarantee company operations function smoothly and efficiently.

5) Ethics, law and regulatory rights [284]-[286]

Another concern for IoT developers is ethics, the rule of law, and regulatory rights. Certain laws and regulations are in place to protect standards and moral values and to keep people from breaching them. The sole difference between ethics and law is that ethics are principles that individuals believe in, whereas laws are limits imposed by the government. However, both ethics and legislation are intended to maintain a high level of quality while discouraging unlawful usage. Several real-world issues have been solved as a result of the development of IoT, but it has also given birth to major ethical and legal difficulties. Some of these problems are data security, privacy protection, trust and safety, and data usability. Because of their lack of faith in IoT devices, the majority of IoT consumers favor government standards and laws regarding data protection, privacy, and safety. As a result, this issue must be addressed in order to retain and strengthen public trust in IoT devices and systems.

6) Scalability, availability and reliability [287]-[301]

A system is scalable if it can add more services, equipment, and devices without affecting how well it works. Supporting a huge number of devices with different amounts of memory, processing power, storage space, and bandwidth is the main problem with IoT. Availability is another important thing to think about. In the IoT's layered structure, scalability and availability should be set up at the same time. Cloud-based IoT solutions are a great example of scalability because they let you add more devices, storage, and processing power to the IoT network as needed. On the other hand, this IoT network that is spread out all over the world leads to a new research paradigm for building a seamless IoT framework that can meet global needs. Another big problem is that it is hard to find resources for real artifacts, no matter where or when they are needed. Several local IoT networks are connected to global IoT platforms in a disorganized way to use their resources and services. Because of this, availability is a very big problem. Some services and resources might not be available because of the use of other ways to send data, like satellite communication. Because of this, it is important to have an independent and reliable data transmission route to make sure that resources and services are always available. 7) Quality of service (QoS) [254], [301]-[311]

Another critical aspect of IoT is QoS. QoS is a metric used to assess the performance, quality, and efficiency of IoT devices, systems, and architecture. Reliability, security, availability, cost, energy consumption, and service time are critical and essential QoS criteria for IoT systems. A more intelligent IoT ecosystem must meet QoS criteria. Furthermore, in order to assure the dependability of any IoT service or device, its QoS metrics must first be specified. Furthermore, consumers may be able to indicate their demands and expectations. A variety of ways may be used to analyze QoS, however there is a trade-off between quality parameters and methodologies. To address this trade-off, high-quality models must be used. Certain high-quality models, such as ISO/IEC25010 and OASIS-web services quality model (WSQM), are accessible in the literature and may be used to evaluate the methodologies employed for QoS assessment. These models include a wide variety of quality parameters that are more than enough for assessing QoS for IoT services.

The IoT has the potential to significantly improve both human quality of life and company efficiency. Through a widely dispersed and locally intelligent network of smart devices, the IoT has the potential to enable extensions and enhancements to fundamental services in a variety of domains, including transportation, logistics, security, utilities, education, healthcare, and other fields. Additionally, it has the potential to provide a new ecosystem for the development of software applications. A concerted effort, motivated by a common understanding of the distinctive nature of the opportunity, is required to move the sector through the initial stages of market growth and into maturity. In terms of service distribution, business and billing procedures, IoT service delivery capabilities, and the many demands these services will place on mobile networks, this market offers distinct qualities. It is hoped that by having a common understanding of IoT characteristics, industry participants would be better able to work together to advance the market for the benefit of consumers and society [8], [50], [311].

5. FUTURE DIRECTIONS

The "internet of things" is hard to leave off a list of creative and game-changing innovations in a world driven by artificial intelligence, data, and constantly improving connection technologies. In fact, IoT may be one of the most important technologies available right now, since it is the reason why many other technologies, like machine learning, work well. As machines get smaller, making things gets easier. In recent years, industries have moved away from a monolithic, single-device architecture and toward a more modular, microservices strategy. A network of devices may do all calculations and measurements instead of a single unit. Each gadget may have its own utility that benefits the overall network. This is the foundation of IoT technology [33], [68], [100], [112], [127], [152], [280].

The value of the IoT technology industry is anticipated to reach $1.39 trillion by 2026. This phenomenal rise is most likely owing to a combination of reasons, including [312]-[314]: 1) the COVTD-19 outbreak has accelerated the development of remote monitoring, smart home devices, and data analysis solutions; and 2) enterprises are competing to build better artificial intelligence solutions. These usually need a network of sophisticated sensors and edge processors in the context of IoT; and 3) IoT networks can perform some tasks more effectively than centralized alternatives.

IoT solutions have gotten more expensive to create as a result of the high demand and limited availability of precious semiconductor chips. The epidemic has only made matters worse. Although manufacture of these chips has grown, the chip scarcity will persist for a long time. Instead of employing a just-in-time approach, some organizations prefer to order their semiconductor supplies months in advance. In 2022, a lack of semiconductors will limit IoT market growth by 10 to 15%. That may appear to be terrible news for companies eager to invest in new IoT solutions, but it isn't the full picture. There's some excellent news on the way. In February 2022, the European Union approved the European Chips Act, a mix of public and private spending aimed at bridging the supply chain gap [232], [315]-[317].

Bandwidth is the most critical constraint in IoT technology. The more the bandwidth the lower the latency of an IoT network. The faster one device can communicate with another, the more fluid and efficient IoT technology will become. If there is too much latency, the Internet of Things offers no advantage over centralized alternatives. High-speed networking was formerly limited by cable and fiber connections. An IoT network may be severely confined if every device is connected by an Ethernet line. Wireless connections may be unstable and provide slower data rates. However, as 5G networks and Wi-Fi 6 have advanced, IoT has risen in prominence in recent years (802.1 lax). The promise of the Internet of Things may be realized with faster wireless data rates than ever before [169], [255], [306].

5.1. Smart homes

The evolution of digital assistants such as Google Assistant, Amazon Echo, Apple's Siri, and others has transformed the smart home industry. With at-home IoT technology capable of handling a wide range of equipment such as lights, appliances, and even home security systems by 2022, the technology looks to have reached a tipping point. However, there is still room for growth as we will see in the next years. According to Mordor Intelligence, the smart home business will grow at a 25% CAGR between now and 2025, reaching a value of $246 billion. One of the next stages will be to focus on smart home automation. Smart house IoT networks are fast extending their ability to automate functions such as lighting, climate control, and security. Customers can adjust them manually or automatically using AI algorithms that examine sensor and use data. MobiDev collaborated with CUJO AI to develop a mobile app that allows consumers to monitor the security of their home network on the IoT smart home device security side [312], [318]-[331].

5.2. Smart cities IoT technology may be utilized in a variety of ways while building smart city networks. Traffic monitoring is one of the most critical challenges. The capacity to monitor traffic with sensors strategically positioned across the city enables better control over intersections and traffic management. Water level monitoring can also be used to detect and warn communities of imminent and current flooding. This information can also be used to guide future flood mitigation activities. Smart streetlights may be used for a variety of purposes. They may be used as sensor platforms to detect weather, traffic, host public Wi-Fi, and perform surveillance. These bulbs can also function as an edge device for digital roadside signs. They might even place the lamps on the light pole itself. Columbus, Ohio's Smart City, has tried self-driving shuttles. This futuristic experiment demonstrates how smart cities may help to decrease the demand for private automobiles in the future [20], [53], [105], [116], [188], [269], [279], [332]-[337].

5.3. IoT technology in healthcare

There are many uses for the IoT technologies in this industry. When used in conjunction with IoT networks, for instance, WebRTC may make it possible to offer more effective telemedicine in locations where edge devices may be more effective than a conventional Internet connection. There are also a great number of usual applications for the IoT, such as sensors in hospital rooms that may keep track of a patient's vital signs throughout the day to assist medical professionals with diagnosis and treatment. The widespread distribution of COVID-19 has accelerated the development of this technology [48], [55], [123], [125], [136], [149], [204], [253], [297], [304], [319], [330], [338]-[341].

5.4. IoT connectivity: 5G, WiFi 6, low power wide area networks (LPWAN), and satellites Wireless data rates have been the most difficult barrier for IoT networks to overcome in recent years. Sensors, edge computing, wearables, smart homes, and other components of IoT technology will improve as these technologies advance. More infrastructure has recently been constructed for additional communication types, making IoT solutions more realistic. 5G, WiFi 6, LPWAN, and satellites are examples of connection technologies. In order to maintain an array of edge devices, sensors, or other devices, the installation of communication infrastructure is required for many IoT technology solutions. Mobile networks, such as LTE, can, nevertheless, be a viable option in some conditions, such as outdoor settings. However, bandwidth constrains 4G LTE. However, 5G networks are substantially quicker and can accommodate the data processing required by IoT networks much more efficiently. WiFi operating on the 6 GHz band significantly increases the interior bandwidth capacity of IoT technologies. The greater the speed at which components in a network can interact, the more reliable the system. WiFi 6 may also be used in houses, which implies it can bring considerable advantages for smart home IoT networks. Low-power wide-area network connectivity is a novel technology that links devices with low bandwidth consumption and low bit rates over greater distances. This makes it a good option for Internet of Things devices that link through machine-to-machine communication. LPWANs are less costly since they utilize less energy. If you need to connect a large number of devices over a large geographical area, LPWAN is a viable option. Satellites can power IoT technology in some circumstances for geographically dispersed networks. Traksat's satellite-powered IoT devices, which are powered by Globalstar satellites, enable humanitarian employees to report emergency occurrences and promptly request help. GPS data is promptly captured and transmitted to HQ for rescue preparations [301], [312], [342]-[351].

5.5. Big data analytics in IoT

An IoT system is made up of a large number of interconnected devices and sensors. As the IoT network grows and expands, the number of these sensors and devices increases. These devices communicate with one another and transfer massive volumes of data via the internet. Because this data is vast and streams every second, it has earned the term "big data." As IoT-based networks proliferate, challenging difficulties such as data management and collection, storage and processing, and analytics emerge. The IoT big data framework for smart buildings is highly useful for dealing with a number of smart building difficulties, such as monitoring oxygen levels, analyzing smoke and harmful compounds, and deciding brightness. This sort of framework is capable of acquiring data from sensors installed in buildings and doing data analytics for decision making. In addition, an IoT-based cyber-physical system outfitted with information analysis and knowledge acquisition approaches might be employed to boost industrial production. Congestion is a major issue in smart cities. Real-time traffic data may be collected and analyzed in an IoT-based traffic management system employing IoT devices and sensors installed at traffic lights. IoT sensors installed on patients provide a vast quantity of data about their health every second in healthcare analysis. To make speedy, correct decisions, this huge volume of data must be integrated into a single database and examined in real time, and big data technology is the appropriate solution. The Internet of Things, in conjunction with big data analytics, may also help in the transition of traditional industrial practices into new ones. The sensing devices generate data that may be analyzed and utilized to help in a number of decision-making processes utilizing big data approaches. In addition, cloud computing and analytics may help with energy development and conservation by cutting costs and enhancing customer satisfaction. Massive volumes of streaming data are generated by IoT devices, which must be efficiently stored and processed in real time for decision-making. Deep learning is quite effective at dealing with such massive amounts of data and can generate highly accurate results. As a result, Internet of Things (IoT), big data analytics, and deep learning are all important to the growth of a high-tech society [22], [28], [74], [77], [89], [92], [93], [101], [103], [110], [114], [126], [149], [152], [155], [183], [188], [202], [232], [265], [318], [352].

5.6. Edge computing: low latency and security

Edge computing is critical for real-time applications. Instead of calculating everything at a central location, edge networks process information closer to the user, lowering total network load for all users. Edge computing has the potential to not only reduce the latency of IoT technology, but also to increase data processing security. When data can be processed on an edge device rather than transmitted to a central server, hackers have less opportunities to intercept it. All that is necessary is for the information to be exchanged with the edge device and then returned to the user. In this case, the information does not need to be remembered. Edge computing is useful in any situation that necessitates quick decisions. This is especially true in circumstances concerning security and safety. One way that IoT edge computing may be used to protect humans is to automatically shut down machines when someone enters a prohibited area at a factory. Autonomous cars require data in order to make critical real-time decisions that might mean the difference between life and death on the road [24], [26], [67], [100], [109], [133], [145], [184], [186], [216], [220], [221], [267], [268], [293], [297], [335].

5.7. Connected networks aid manufacturers

IoT technology has huge potential to improve the industrial company. This industry has become more automated than ever before, thanks to arrays of sensors installed on production floors. One of the most significant results of the proliferation of IoT sensors in manufacturing is that these networks now offer sophisticated artificial intelligence applications. Without sensor data, AI cannot offer solutions such as predictive maintenance, defect detection, digital twins, and generative design. The future of IoT technology lies in each of the previous developments and beyond, with applications ranging from retail to indoor navigation. It's also important to realize that the Internet of Things isn't a stand-alone technology in many cases. Combining technologies such as IoT and AI helps businesses to stimulate innovation while remaining competitive. All it needs to get started is a notion or a vision for upgrading your company's infrastructure in order to confront the future head on [312], [313], [353]-[362].

5.8. Wearable IoT Technology

While sensors and edge devices are critical components of many IoT technology solutions, wearable IoT devices must not be overlooked. Wearable IoT products such as smart watches, earbuds, and extended reality (AR/VR) headsets are making waves in 2022 and will only grow better. Wearable technology, particularly smartwatches, was predicted to someday replace smartphones and desktop computers. However, it does not appear that this forecast will become a reality very soon. Wearable gadgets, such as smartwatches, are less likely to be beneficial for tasks that can be completed on a smartphone or laptop due to their restricted practicality. Wearable IoT technology, on the other hand, has huge potential to assist in medical activities because to its ability to monitor patient vitals. These devices can do things like immediately warn others in the case of an emergency and collect continuous health data. If IoT technology can provide devices with real-time contextual data, it's simple to see how this can improve the performance of augmented reality (AR) and virtual reality (VR) devices. Sensor data and real-time network information shown on a head-mounted display might be particularly beneficial in specific scenarios for professionals [120], [156], [322], [330], [331]: 1) head-mounted AR assists surgeons during surgery by providing data from wearables and other sensors; 2) a manufacturer is studying a virtual reality digital twin simulation of an industrial layout. In order to compare performance, IoT sensor data from the actual production floor may be shown alongside the digital twin, and 3) IoT devices help locate the item and transmit directions to the worker as a distribution center employee uses augmented reality on a mobile smartphone to locate a specific item in a warehouse.

5.9. Artificial intelligence and IoT technology (AIoT)

The ability to allow AI software is one of the most exciting uses of IoT technology. AI and the Internet of Things are mutually beneficial. IoT improves AI by dispersing data, and IoT benefits AI by improving management. IoT sensors are a significant addition to the machine learning data pipeline since AI technologies are essentially data-driven. By 2026, the value of AI in IoT technologies would be $ 14,799 million High-quality data is essential for machine learning algorithms to be effective. For example, real-time data from IoT sensors monitoring industrial equipment may help machine learning algorithms forecast when equipment will need to be fixed in the future, a process known as predictive maintenance and one of the most important applications of AI in manufacturing. Visual inspection is another area where IoT technology and AI combine to improve the industrial and distribution industries. Watch our video on AI-driven visual inspection tools to see this in action. Although machine learning is extremely adept at detecting patterns, it cannot do so in the absence of high-quality data. Machine learning will grow increasingly popular in the next years as bandwidth expands and IoT networks take center stage in a variety of industries [353], [363]-[380].

5.10. Metaverse with the internet of things

IoT will allow virtual spaces to interact and access the real world seamlessly whereas Metaverse technology will offer the necessary 3D user interface for the IoT device cluster. This will provide a user-centered IoT and metaverse experience to the users. This means that IoT's main objective will be to provide the necessary link between the outside world and the virtual world. IoT stands for the internet of three-dimensional physical things. The metaverse has a 3D user interface, allowing for a more personalized user interface environment for IoT. As a result, the metaverse connects the real and digital worlds, allowing people and things to work together more naturally in complicated circumstances. As a 3D interface for IoT, the metaverse will render the real and digital indistinguishable, enhancing our human ability to make better-informed judgments [381]-[389].

Virtual simulations employing digital twins in the metaverse allow us to design and evaluate training techniques in conditions that we cannot perform in the actual world. A digital twin is a virtual representation of a real-world physical system or process (a physical twin) that serves as the indistinguishable digital counterpart of it for practical purposes, such as system simulation, integration, testing, monitoring, and maintenance. Digital twins can vehicles, buildings, factories, or people. The most complex digital twin is the Digital Human or Virtual Human. A digital twin may be used in real time and regularly synchronized with the corresponding physical system. As the metaverse becomes more realistic, training simulations will assist equip people and Al/software to collaborate to spot problems and limit their effect in real life. Complex virtual simulation will enable us to run multiple long-term planning scenarios and decide the best designs for our energy, transportation, and healthcare systems, as well as to dynamically operate these techniques as the real-world evolves. The metaverse, enabled by AR and IoT, will set an altogether new standard for work and collaboration, allowing organizations to function at far higher speed and scale [381]-[389].

The primary component of perception of the elements in the environment is critical for the second generation of virtual people since it informs them of what is changing and what is static. A virtual human's position and activity must be constantly updated, which alters its perceptions. As a result, sensory information and IoT have a significant impact on virtual human behavior. Sensors that mimic the functioning of their biological counterparts, namely for vision, audition, and tactile sense, should be used in virtual humans. Virtual senses convey decisions about the limb motion allowing not only to avoid obstacles, but also to walk on uneven terrains and climb stairs or even jump in case of narrow obstacles like little river or rocks. To obtain a genuine feeling of autonomy - a core human trait - virtual individuals should have an internal representation of self that includes drives, motivation, personality, and emotion. Such internal states can then control the behavior of virtual humans in reaction to events and interact in a dynamic real-time environment.

The main problem here is to create a cohesive computational model that replicates these internal traits and their influence on the reasoning process and the resulting behavior of the virtual people. Empowering Virtual Humans to better interact with us and offer us quality based services is the ultimate goal of Metaverse. It is the ultimate goal to empower our capacity to learn, to get help if we have disabilities, to prepare us in advance for unexpected events and enable us to answer the question "what will happen if [381]-[389].

IoT is set to transform the metaverse at it seamlessly connects the 3D world to a large number of real-life devices. The real connection between IoT and the metaverse relies on cloud technology. The smooth interoperability of AR and IoT data will be critical to the metaverse' s success, contributing to a common vision of user-centric multimedia services for modern nomadic people with high levels of mobility and network connectivity most of the time. By reducing spatial limits in our everyday activities, it will expand personal media spaces for modern nomadic existence.

6. CONCLUSION

The IoT has swiftly expanded and enhanced operations in a wide range of real-world applications, from consumer, commercial, manufacturing, and industrial IoT, including smart homes, smart cities, wearables, healthcare, big data analytics, edge computing, connected networks, aid manufacturers, AI, and metaverse. This paper has covered the current state of the IoT ecosystem; its key uses and benefits; important architectural stages; some of the problems and challenges it faces; and its future. This paper has also described how an adequate IoT architecture that preserves data, analyzes it, and suggests corrective action enhances the process's ground reality. This IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. An IoT architecture that preserves, analyzes, and offers corrective action might improve the process. This IoT system architecture is comprised of devices, gateways, and platforms. The data is then collected, assessed, processed, and remedial solutions are proposed by high-end apps. This appealing combination provides great benefit through automatic action. The value of the automatic action provided by this sophisticated combination is exceptionally high. In the future, there will be many more technologies built upon the IoT. Over the next few years, machine learning is likely to become more popular as IoT networks become more important in more fields.

BIOGRAPHIES OF AUTHORS

Tole Sutikno '? is a lecturer in the Electrical Engineering Department at the

Universitas Ahmad Dahlan (UAD), Yogyakarta, Indonesia. He received his B.Eng., MEng., and Ph.D. degrees in Electrical Engineering from Universitas Diponegoro, Universitas GadjahMada, and Universiti Teknologi Malaysia, in 1999,2004, and 2016, respectively. He has been an Associate Professor at UAD, Yogyakarta, Indonesia since 2008. He is currently the Editor-in-Chief of the Bulletin of Electrical Engineering and Informatics and the Head of the Embedded Systems and Power Electronics Research Group. His research interests include the fields of digital design, industrial applications, industrial electronics, industrial informatics, power electronics, motor drives, renewable energy, FPGA applications, embedded systems, artificial intelligence, intelligent systems, information systems, and digital libraries. He can be contacted at email: [email protected].

Daniel Thalmann (c) Ei H '5 is a Swiss-Canadian computer scientist and virtual human pioneer. He is an Honorary Professor at EPFL, Switzerland, and Director of Research Development at MIRALab Sari, Geneva. After a master's in nuclear physics (1970) and a certificate in statistics and computer science (1972) from the University of Geneva, he got a PhD in computer science (1977). His PhD work focused on abstract machines for portable compilers and operating systems. He taught at the University of Montreal in Canada from 1977 until 1989, where he worked on computer graphics and animation. He returned to Switzerland and started VRLab at EPFL. CERN, the University of Nebraska-Lincoln, the University of Tokyo, and the National University of Singapore all have him as a visiting professor or researcher. From 2009 until 2017, he was a Visiting Professor at NTU's Institute for Media Innovation. He's co-editor-in-chief of the Journal of Computer Animation and Virtual Worlds and a member of six others. Thalmann has published over 650 graphics, animations, and VR publications. He coedited 30 volumes and wrote Stepping into Virtual Reality (2007) and Crowd Simulation (2012) for Springer. Thalmann received an Honorary Doctorate from Toulouse's Paul-Sabatier University in 2003. In 2010, he won the Eurographics Distinguished Career Award, and in 2012, the Canadian Human Computer Communications Society Achievement Award. He won the CGI Career Achievement Award from the Computer Graphics Society (CGS) in 2015. Real-time virtual humans in virtual reality, simulating crowds, and 3D interaction are some of his current research interests. He can be contacted at email: [email protected].

References

REFERENCES

[1] K. Ashton, "That 'internet of things' thing," RFID J., vol. 22, no. 7, pp. 97-114, 2009.

[2] M. Weiser, "The computer for the 21st century," ACMSIGMOBILEMob. Comput. Commun. Rev., vol. 3, no. 3, pp. 3-11, 1999, doi: https://doi.org/10.1145/329124.329126.

[3] L. S. Vailshery, "Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2021, with forecasts from 2022 to 2030," Statista, 2022. https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/.

[4] K. Jhajharia and P. Mathur, "A comprehensive review on machine learning in agriculture domain," IAES Int. J. Artif. Intell, vol. 11, no. 2, pp. 753-763, 2022, doi: 10.11591/ijai.vll.i2.pp753-763.

[5] J. Lin, W. Long, A. Zhang, and Y. Chai, "Blockchain and IoT-based architecture design for intellectual property protection," Int. J. CrowdSci., vol. 4, no. 3, pp. 283-293, 2020, doi: 10.1108/IJCS-03-2020-0007.

[6] M. A. Torad, B. Bouallegue, and A. M. Ahmed, "A voice controlled smart home automation system using artificial intelligent and internet of things," Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 4, pp. 808-816, 2022, doi: 10.12928/TELKOMNIKA. v20i4.23763.

[7] M. Elkholy and M. A. Marzok, "Light weight serverless computing at fog nodes for internet of things systems," Indones. J. Electr. Eng. Comput. Set, vol. 26, no. 1, pp. 394-403, 2022, doi: 10.11591/ijeecs.v26.il.pp394-403.

[8] B. Ambore and L. Suresh, "Novel model for boosting security strength and energy efficiency in internet-of-things using multi-staged game,"/"/. J. Electr. Comput. Eng., vol. 9, no. 5, pp. 4326^1335, 2019, doi: 10.11591/ijece.v9i5.pp4326-4335. rr\n t-1" J "\ta a i j jooi'I j j1 tti-1" "j-'J "j." ¦ i j ¦ j. j." j ¦ ¦ j. j."

[9J firdaus, N. A. Ahmad, and S. Sahibuddin, fingerprint indoor positioning based on user orientations and minimum computation time," Telkomnika (Telecommunication Comput. Electron. Control, vol. 17, no. 4, pp. 1740-1749, 2019, doi: 10.12928/TELKOMNIKA.V17I4.12774.

[10] I. Idrissi, M. Azizi, and O. Moussaoui, "An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 2, pp. 1140-1150, 2022, doi: 10.11591/ijeecs.v25.i2.ppl 140-1150.

[11] D. Karthikeyan et al, "Sophisticated and modernized library running system with OCR algorithm using IoT," Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1680-1691, 2021, doi: 10.11591/ijeecs.v24.i3.ppl680-1691.

[12] R. J. Kavitha and K. K. Saravanan, "Digital brain: Model-based framework for dependable electroencephalogram sensing and actuation in internet of things system," Int. J. Reconfigurable Embed. Syst., vol. 10, no. 3, pp. 168-175, 2021, doi: 10.11591/IJRES.V10.I3.PP168-175.

[13] M. A. Omran, W. K. Saad, B. J. Hamza, and A. Fahe, "A survey of various intelligent home applications using IoT and intelligent controllers,"Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 1, pp. 490^199, 2021, doi: 10.11591/ijeecs.v23.il.pp490-499.

[14] F. D. S. Sumadi, A. E. Minarno, L. Syafa'ah, and M. Irfan, "Enabling seamless communication over several IoT messaging protocols in OpenFlow network," Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 5, pp. 1544-1552, 2021, doi: 10.12928/TELKOMNIKA.vl9i5.20412.

[15] M. S. Mahmoud, D. A. Hammood, and A. Alkhayyat, "Integrating device to device network with internet of health things: Towards minimum power allocation," Bull. Electr. Eng. Informatics, vol. 9, no. 6, pp. 2334-2341, 2020, doi: 10.11591/eei.v9i6.2491.

[16] S. Villamil, C. Hernandez, and G. Tarazona, "An overview of internet of things," Telkomnika (Telecommunication Comput. Electron. Control, vol. 18, no. 5, pp. 2320-2327, 2020, doi: 10.12928/TELKOMNIKA.vl8i5.15911.

[17] N. R. Krishnamoorthy, I. Rajkumar, J. Alexander, and D. Marshiana, "Performance analysis of bio-Signal processing in ocean Environment using soft computing techniques," Int. J. Electr. Comput. Eng., vol. 10, no. 3, pp. 2944-2950, 2020, doi: 10.11591/ijece.vl0i3.pp2944-2950.

[18] R. Dhaya and R. Kanthavel, "IoT based urban flooding high definition surveillance using concurrent multipath wireless system," Earth Sci. Informatics, vol. 15, no. 3, pp. 1407-1416, 2022, doi: 10.1007/sl2145-022-00817-4.

[19] A. Khan, S. Gupta, and S. K. Gupta, "Emerging UAV technology for disaster detection, mitigation, response, and preparedness," J. F. Robot, vol. 39, no. 6, pp. 905-955, 2022, doi: 10.1002/rob.22075.

[20] P. M. Rao and B. D. Deebak, "Security and privacy issues in smart cities/industries: technologies, applications, and challenges," J. AmbientIntell. Humaniz. Comput., 2022, doi: 10.1007/sl2652-022-03707-l.

[21] N. J. Habeeb and S. T. Weli, "Combination of GIS with Different Technologies for Water Quality: An Overview," HighTech Innov. J., vol. 2, no. 3, pp. 262-272, 2021, doi: 10.28991/HIJ-2021-02-03-10.

[22] M. U. Simsek, F. Yildirim Okay, and S. Ozdemir, "A deep learning-based CEP rule extraction framework for IoT data," J. Supercomput, vol. 77, no. 8, pp. 8563-8592, 2021, doi: 10.1007/sl 1227-020-03603-5.

[23] W. Osamy, A. M. Khedr, A. A. El-Sawy, A. Salim, and D. Vijayan, "Ipdca: Intelligent proficient data collection approach for iot-enabled wireless sensor networks in smart environments," Electron., vol. 10, no. 9, 2021, doi: 10.3390/electronicsl0090997.

[24] Y. Mansouri and M. A. Babar, "A review of edge computing: Features and resource virtualization," J. Parallel Distrib. Comput., vol. 150, pp. 155-183, 2021, doi: 10.1016/j.jpdc.2020.12.015.

[25] J. Pang, Y. Huang, Z. Xie, Q. Han, and Z. Cai, "Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT," IEEE Internet Things!, vol. 8, no. 5, pp. 3088-3098, 2021, doi: 10.1109/JIOT.2020.3007662.

[26] D. Di Luccio, S. Kosta, A. Castiglione, A. Maratea, and R. Montella, "Vessel to shore data movement through the Internet of Floating Things: A microservice platform at the edge," Concurr. Comput. Pract. Exp., vol. 33, no. 4, 2021, doi: 10.1002/cpe.5988.

[27] C. Xie, B. Yu, Z. Zeng, Y. Yang, and Q. Liu, "Multilayer Internet-of-Things Middleware Based on Knowledge Graph," IEEE Internet Things!, vol. 8, no. 4, pp. 2635-2648, 2021, doi: 10.1109/JIOT.2020.3019707.

[28] M. Ersoy and B. Aksoy, "Deep learning techniques of losses in data transmitted in wireless sensor networks," Turkish!. Electr. Eng. Comput. Sci., vol. 29, no. 2, pp. 583-597, 2021, doi: 10.3906/ELK-2001-145.

[29] G Khadka, B. Ray, N. C. Karmakar, and J. Choi, "Physical-Layer Detection and Security of Printed Chipless RFID Tag for Internet of Things Applications," IEEE Internet Things!, vol. 9, no. 17, pp. 15714-15724, 2022, doi: 10.1109/JIOT.2022.3151364.

[30] A. Jamalipour and S. Murali, "A Taxonomy of Machine-Learning-Based Intrusion Detection Systems for the Internet of Things: A Survey," IEEE Internet Things!, vol. 9, no. 12, pp. 9444-9466, 2022, doi: 10.1109/JIOT.2021.3126811.

[31] B. Nagajayanthi, "Decades of Internet of Things Towards Twenty-first Century: A Research-Based Introspective," Wirel. Pers. Commun., vol. 123, no. 4, pp. 3661-3697, 2022, doi: 10.1007/sll277-021-09308-z.

[32] J. E. Balota and A.-L. Kor, "Brokerage System for Integration of LrWPAN Technologies," Sensors, vol. 22, no. 5, 2022, doi: 10.3390/s22051733.

[33] H. Wang et al, "A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT," IEEE Internet Things!, vol. 9, no. 2, pp. 939-963, 2022, doi: 10.1109/JIOT.2021.3111624.

[34] A. Fortas, E. Kerkouche, and A. Chaoui, "Formal verification of IoT applications using rewriting logic: An MDE-based approach," Sci. Comput. Program., 2022, doi: 10.1016/j.scico.2022.102859.

[35] A. G Putrada, M. Abdurohman, D. Perdana, and H. H. Nuha, "Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey," IEEE Access, vol. 10, pp. 45137^15178, 2022, doi: 10.1109/ACCESS.2022.3169765.

[36] M. F. Ali, D. N. K. Jayakody, and Y. Li, "Recent Trends in Underwater Visible Light Communication (UVLC) Systems," IEEE Access, vol. 10, pp. 22169-22225, 2022, doi: 10.1109/ACCESS.2022.3150093.

[37] X. Zhang, X. Ming, Y. Bao, and X. Liao, "System construction for comprehensive industrial ecosystem oriented networked collaborative manufacturing platform (NCMP) based on three chains," Adv. Eng. Informatics, vol. 52, 2022, doi: 10.1016/j.aei.2022.101538.

[38] H. Golpira, S. A. R. Khan, and S. Safaeipour, "A review of logistics Internet-of-Things: Current trends and scope for future research," J. lnd. Inf. Integr., vol. 22, 2021, doi: 10.1016/j.jii.2020.100194.

[39] A. S. Kaittan, S. M. Hameed, N. K. Ali, and M. H. Ali, "Smart management system for monitoring and control of infant baby bed," Int.! Electr. Comput. Eng, vol. 10, no. 5, pp. 5025-5031, 2020, doi: 10.11591/IJECE.V10I5.PP5025-5031.

[40] P. Chandana et al., "An effective identification of crop diseases using faster region based convolutional neural network and expert systems," Int.! Electr. Comput. Eng, vol. 10, no. 6, pp. 6531-6540, 2020, doi: 10.11591/IJECE.V10I6.PP6531-6540.

[41] M. A. Al-Shareeda, M. Anbar, M. A. Alazzawi, S. Manickam, and I. H. Hasbullah, "Security schemes based on conditional privacy-preserving vehicular ad hoc networks," Indones.!. Electr. Eng. Comput. Sci., vol. 21, no. 1, pp. 479-488, 2021, doi: 10.11591/ijeecs.v21.il.pp479-488.

[42] Abdurrasyid, Indrianto, M. N. I. Susanti, and Y. S. Purwanto, "Detection of water quality in crayfish ponds with IoT," Bull. Electr. Eng. Informatics, vol. 10, no. 2, pp. 886-897, 2021, doi: 10.11591/eei.vl0i2.1968.

[43] V. Simadiputra and N. Surantha, "Rasefiberry: Secure and efficient raspberry-pi based gateway for smarthome iot architecture," Bull. Electr. Eng. Informatics, vol. 10, no. 2, pp. 1035-1045, 2021, doi: 10.11591/eei.vl0i2.2741.

[44] V. H. Cespedes, G Y. Florez, and Y. A. Garces-Gomez, "The internet of things in high andean wetland monitoring, historical review approach," Bull. Electr. Eng. Informatics, vol. 10, no. 3, pp. 1572-1579, 2021, doi: 10.11591/eei.vl0i3.2653.

[45] C. C. Uchenna, N. Jamil, R. Ismail, L. K Yan, and M. A. Mohamed, "Malware threat analysis techniques and approaches for iot applications: A review," Bull. Electr. Eng. Informatics, vol. 10, no. 3, pp. 1558-1571, 2021, doi: 10.11591/eei.vl0i3.2423.

[46] J. Karande and S. Joshi, "DEDA: An algorithm for early detection of topology attacks in the internet of things," Int.!. Electr. Comput. Eng, vol. 11, no. 2, pp. 1761-1770, 2021, doi: 10.11591/ijece.vlli2.ppl761-1770.

[47] F. Khan, R. Lakshmana Kumar, S. Kadry, Y. Nam, and M. N. Meqdad, "Cyber physical systems: A smart city perspective," Int.!. Electr. Comput. Eng, vol. 11, no. 4, pp. 3609-3616, 2021, doi: 10.11591/ijece.vlli4.pp3609-3616.

[48] H. A. Khan, R. Abdulla, S. K. Selvaperumal, and A. Bathich, "IoT based on secure personal healthcare using RFID technology and steganography,"Int.!. Electr. Comput. Eng, vol. 11, no. 4, pp. 3300-3309, 2021, doi: 10.11591/ijece.vlli4.pp3300-3309.

[49] W. G Hatcher, C. Qian, W. Gao, F. Liang, K. Hua, and W. Yu, "Towards Efficient and Intelligent Internet of Things Search Engine," IEEE Access, vol. 9, pp. 15778-15795, 2021, doi: 10.1109/ACCESS.2021.3052759.

[50] S. M. Sharath, P. Manjunatha, and H. R. Shwetha, "Insights on critical energy efficiency approaches in internet-of-things application," Int.!. Electr. Comput. Eng, vol. 11, no. 4, pp. 2925-2933, 2021, doi: 10.11591/ijece.vlli4.pp2925-2933.

[51] P. Tangwannawit and K. Saengkrajang, "An internet of things ecosystem for planting of coriander (Coriandram sativum L.)," Int. J. Electr. Comput. Eng., vol. 11, no. 5, pp. 4568^1576, 2021, doi: 10.11591/ijece.vlli5.pp4568-4576.

[52] P. Atmaja and N. Surantha, "Smart hydroponic based on nutrient film technique and multistep fuzzy logic," Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 3146-3157, 2022, doi: 10.11591/ijece.vl2i3.pp3146-3157.

[53] V. K. Quy, P. M. Chuan, and L. A. Ngoc, "An improved performance routing protocol based on delay for MANETs in smart cities," Int. J. Electr. Comput. Eng, vol. 12, no. 1, pp. 418^124, 2022, doi: 10.11591/ijece.vl2il.pp418-424.

[54] S. Tangwannawit and P. Tangwannawit, "An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture," IAES Int. J. Artif. Intell., vol. 11, no. 1, pp. 201-209, 2022, doi: 10.11591/ijai.vll.il.pp201-209.

[55] A. N. Kadhim and M. E. Manaa, "Design an efficient internet of things data compression for healthcare applications," Bull. Electr. Eng. Informatics, vol. 11, no. 3, pp. 1678-1686, 2022, doi: 10.11591/eei.vl H3.3758.

[56] A. M. Shrif, A. A. Gouda, and M. A. Razek, "Comparative study among constrained application protocol extensible messaging, and presence protocol of IoT," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 546-554, 2022, doi: 10.11591/ijeecs.v27.il.pp546-554.

[57] A. H. Mohammed and R. M. A. Hussein, "security services for internet of thing smart health care solutions based blockchain technology," Telkomnika (Telecommunication Comput. Electron. Control, vol. 20, no. 4, pp. 772-779, 2022, doi: 10.12928/TELKOMNIKA.v20i4.23765.

[58] P. Iyappan et al, "A generic and smart automation system for home using internet of things," Bull. Electr. Eng. Informatics, vol. 11, no. 5, pp. 2727-2736, 2022, doi: 10.11591/eei.vlli5.3785.

[59] C. Vijayakumaran, B. Muthusenthil, and B. Manickavasagam, "A reliable next generation cyber security architecture for industrial internet of things environment,"/"/. J. Electr. Comput. Eng., vol. 10, no. 1, pp. 387-395, 2020, doi: 10.11591/ijece.vl0il.pp387-395.

[60] M. F. Mohamed Firdhous and B. H. Sudantha, "{Cloud, IoT}-powered smart weather station for microclimate monitoring,"Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 1, pp. 508-515, 2020, doi: 10.11591/ijeecs.vl7.il.pp508-515.

[61] V.-P. Hoang, M.-H. Nguyen, T. Q. Do, D.-N. Le, and D. D. Bui, "A long range, energy efficient Internet of Things based drought monitoring system," Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 1278-1287, 2020, doi: 10.11591/ijece.vl0i2.ppl278-1287.

[62] Z. Kasiran, S. Abdullah, and N. M. Nor, "An advance encryption standard cryptosystem in iot transaction," Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 3, pp. 1548-1554, 2020, doi: 10.11591/ijeecs.vl7.i3.ppl548-1554.

[63] F. G. Abdulkadhim, Z. Yi, C. Tang, M. Khalid, and S. A. Waheeb, "A survey on the applications of IoT: An investigation into existing environments, present challenges and future opportunities," Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 3, pp. 1447-1458, 2020, doi: 10.12928/TELKOMNIKA.vl8i3.15604.

[64] J. K. Park and J. Kim, "Smart fire monitoring system with remote control using ZigBee network," Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 2, pp. 1132-1139, 2020, doi: 10.11591/ijeecs.v21.i2.ppll32-1139.

[65] K. Nagarathna, "Energy-aware strategy for data forwarding in IoT ecosystem," Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 4863^1871, 2020, doi: 10.11591/ijece.vl0i5.pp4863-4871.

[66] M. I. Zakaria, M. N. Norizan, M. M. Isa, M. F. Jamlos, and M. Mustapa, "Comparative analysis on virtual private network in the internet of things gateways," Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 1, pp. 488-497, 2022, doi: 10.11591/ijeecs.v28.il.pp488-497.

[67] R. Yauri, A. Castro, R. Espino, and S. Gamarra, "Implementation of a sensor node for monitoring and classification of physiological signals in an edge computing system," Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 1, pp. 98-105, 2022, doi: 10.11591/ijeecs.v28.il.pp98-105.

[68] R. Mahzabin, F. H. Sifat, S. Anjum, A.-A. Nayan, and M. G. Kibria, "Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 447-455, 2022, doi: 10.11591/ijeecs.v27.il.pp447-455.

[69] P. Shanmurthy, P. Thangamuthu, B. Balusamy, and S. Kadry, "Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 504-512, 2022, doi: 10.11591/ijeecs.v27.il.pp504-512.

[70] S. W. Nourildean, M. D. Hassib, and Y. A. Mohammed, "Internet of things based wireless sensor network: a review," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 246-261, 2022, doi: 10.11591/ijeecs.v27.il.pp246-261.

[71] Indrabayu, I. S. Areni, A. Bustamin, and R. Irianty, "A real-time data association of internet of things based for expert weather station system," IAES Int. J. Artif. Intell., vol. 11, no. 2, pp. 432-439, 2022, doi: 10.11591/ijai.vl 1 i2.pp432-439.

[72] C. Jittawiriyanukoon and V. Srisarkun, "Simulation for predictive maintenance using weighted training algorithms in machine learning," Int. J. Electr. Comput. Eng, vol. 12, no. 3, pp. 2839-2846, 2022, doi: 10.11591/ijece.vl2i3.pp2839-2846.

[73] Y. M. Mohialden, N. M. Hussien, Q. A. Z. Jabbar, M. A. Mohammed, and T. Sutikno, "An internet of things-based medication validity monitoring system," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 932-938, 2022, doi: 10.11591/ijeecs.v26.i2.pp932-938.

[74] B. I. Farhan and A. D. Jasim, "Performance analysis of intrusion detection for deep learning model based on CSE-CIC-IDS2018 dataset," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 1165-1172, 2022, doi: 10.11591/ijeecs.v26.i2.ppl 165-1172.

[75] A. D. Mukhamejanova, E. A. Grabs, K. K. Tumanbayeva, and E. M. Lechshinskaya, "Traffic simulation in the LoRaWAN network," Bull. Electr. Eng. Informatics, vol. 11, no. 2, pp. 1117-1125, 2022, doi: 10.11591/eei.vl H2.3484.

[76] M. H. Ishak, M. S. Mispan, W. Y. Chiew, M. R. Kamaruddin, and M. A. Korobkov, "Secure lightweight obfuscated delay-based physical unclonable function design on FPGA," Bull. Electr. Eng. Informatics, vol. 11, no. 2, pp. 1075-1083, 2022, doi: 10.11591/eei.vlli2.3265.

[77] M. Al-Shabi and A. Abuhamdah, "Using deep learning to detecting abnormal behavior in internet of things," Int. J. Electr. Comput. Eng, vol. 12, no. 2, pp. 2108-2120, 2022, doi: 10.11591/ijece.vl2i2.pp2108-2120.

[78] M. Al-Shabi and A. Al-Qarafi, "Improving blockchain security for the internet of things: challenges and solutions," Int. J. Electr. Comput. Eng, vol. 12, no. 5, pp. 5619-5629, 2022, doi: 10.11591/ijece.vl2i5.pp5619-5629.

[79] N. P. Sodinapalli, S. Kulakrni, N. A. Sharief, and P. Venkatareddy, "An efficient resource utilization technique for scheduling scientific workload in cloud computing environment," IAES Int. J. Artif. Intell, vol. 11, no. 1, pp. 367-378, 2022, doi: 10.11591/ijai.vl 1 .il .pp367-378.

[80] K. H. K. Khoshnaw, Z. A. A. Shwany, T. Mustafa, and S. K. Ismail, "Mobile recommender system based on smart city graph," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, pp. 1771-1776, 2022, doi: 10.11591/ijeecs.v25.i3.ppl771-1776.

[81] R. Mahakud, B. K. Pattanayak, and B. Pati, "Internet of things and multi-class deep feature-fusion based classification of tomato leaf disease," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 2, pp. 995-1002, 2022, doi: 10.11591/ijeecs.v25.i2.pp995-1002.

[82] A. Chahal, P. Gulia, and N. S. Gill, "Different analytical frameworks and bigdata model for internet of things," Indones. J. Electr.

Eng. Comput. Sci., vol. 25, no. 2, pp. 1159-1166, 2022, doi: 10.11591/ijeecs.v25.i2.ppll59-1166.

[83] A. Sattar, Y. A. Shampod, M. T. Ahmed, N. Akter, and A. Mahmud, "Deployment of e-services based contextual smart agro system using internet of things," Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 414-425, 2022, doi: 10.11591/eei.vl lil.3255.

[84] K.-K. Kee, S. L. B. Yew, Y. S. Lim, Y. P. Ting, and R. Rashidi, "Universal cyber physical system, a prototype for predictive maintenance," Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 42-49, 2022, doi: 10.11591/eei.vllil.3216.

[85] X.-K. Dang, L. A.-H. Ho, X.-P. Nguyen, and B.-L. Mai, "Applying artificial intelligence for the application of bridges deterioration detection system," Telkomnika (Telecommunication Comput. Electron. Control, vol. 20, no. 1, pp. 149-157, 2022, doi: 10.12928/TELKOMNIKA.v20i 1.20783.

[86] R. J. Mohammed, E. A. Abed, and M. M. Elgayar, "Comparative study between metaheuristic algorithms for internet of things wireless nodes localization," Int. J. Electr. Comput. Eng., vol. 12, no. 1, pp. 660-668, 2022, doi: 10.11591/ijece.vl2il.pp660-668.

[87] A. F. Alshudukhi, S. A. Jabbar, and B. Alshaikhdeeb, "A feature selection method based on auto-encoder for internet of things intrusion detection," Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 3265-3275, 2022, doi: 10.11591/ijece.vl2i3.pp3265-3275.

[88] M. Y. Hasan and D. J. Kadhim, "A new smart approach of an efficient energy consumption management by using a machine-learning technique," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 1, pp. 68-78, 2022, doi: 10.11591/ijeecs.v25.il.pp68-78.

[89] O. Sbai and M. Elboukhari, "Deep learning intrusion detection system for mobile ad hoc networks against flooding attacks," IAES Int. J. Artif Intell, vol. 11, no. 3, pp. 878-885, 2022, doi: 10.11591/ijai.vll.i3.pp878-885.

[90] S. Amassmir, S. Tkatek, O. Abdoun, and J. Abouchabaka, "An intelligent irrigation system based on internet of things to minimize water loss," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 1, pp. 504-510, 2022, doi: 10.11591/ijeecs.v25.il.pp504-510.

[91] A. S. Ahmed and H. A. Salah, "The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 1, pp. 273-280, 2022, doi: 10.11591/ijeecs.v25.il.pp273-280.

[92] L. Rabhi, N. Falih, L. Afraites, and B. Bouikhalene, "A functional framework based on big data analytics for smart farming," Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1772-1779, 2021, doi: 10.11591/ijeecs.v24.i3.ppl772-1779.

[93] H. M. Nasir, N. M. A. Brahin, M. M. M. Aminuddin, M.S. Mispan, and M. F. Zulkifli, "Android based application for visually impaired using deep learning approach," IAES Int. J. Artif. Intell, vol. 10, no. 4, pp. 879-888, 2021, doi: 10.11591/ijai.vl0.i4.pp879-888.

[94] V. Sithole and L. Marshall, "Mining knowledge graphs to map heterogeneous relations between the internet of things patterns," Int. J. Electr. Comput. Eng, vol. 11, no. 6, pp. 5066-5080, 2021, doi: 10.11591/ijece.vlli6.pp5066-5080.

[95] M. Karthikeyan and T. S. Subashini, "Automated object detection of mechanical fasteners using faster region based convolutional neural networks," Int. J. Electr. Comput. Eng., vol. 11, no. 6, pp. 5430-5437, 2021, doi: 10.11591/ijece.vl li6.pp5430-5437.

[96] R. R. Nuiaa, S. Manickam, and A. H. Alsaeedi, "Distributed reflection denial of service attack: A critical review," Int. J. Electr. Comput. Eng, vol. 11, no. 6, pp. 5327-5341, 2021, doi: 10.11591/ijece.vl Ii6.pp5327-5341.

[97] F. Z. Fagroud, E. H. B. Lahmar, H. Toumi, Y. Baddi, and S. E. Filali, "Rt-rct: An online tool for real-time retrieval of connected things," Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2804-2810, 2021, doi: 10.11591/eei.vl0i5.2901.

[98] E. A. Cardenas-Lancheros and N. E. Vera-Parra, "Incident forecasting model for motorcycle driving based on IoT and artificial intelligence,"Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 1, pp. 444^151, 2021, doi: 10.11591/ijeecs.v24.il.pp444-451.

[99] M. R. Belgaum, Z. Alansari, S. Musa, M. M. Alain, and M. S. Mazliham, "Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues," Int. J. Electr. Comput. Eng., vol. 11, no. 5, pp. 4458-4470, 2021, doi: 10.11591/ijece.vlli5.pp4458-4470.

[100] M. Grari, I. Idrissi, M. Boukabous, O. Moussaoui, M. Azizi, and M. Moussaoui, "Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 2, pp. 1062-1073, 2022, doi: 10.11591/ijeecs.v27.i2.ppl062-1073.

[101] Sharipuddin et al, "Intrusion detection with deep learning on internet of things heterogeneous network," IAES Int. J. Artif. Intell, vol. 10, no. 3, pp. 735-742, 2021, doi: 10.11591/ijai.vl0.i3.pp735-742.

[102] P. C. Siswipraptini, R. N. Aziza, I. Sangadji, Indrianto, R. R. A. Siregar, and G. Sondakh, "IoT for smart home system," Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 2, pp. 733-739, 2021, doi: 10.11591/ijeecs.v23.i2.pp733-739.

[103] I. Idrissi, M. Azizi, and O. Moussaoui, "Accelerating the update of a DL-based IDS for IoT using deep transfer learning," Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 2, pp. 1059-1067, 2021, doi: 10.11591/ijeecs.v23.i2.ppl059-1067.

[104] A. Afridi et al, "An efficient and improved model for power theft detection in pakistan," Bull. Electr. Eng. Informatics, vol. 10, no. 4, pp. 1828-1837, 2021, doi: 10.11591/eei.vl0i4.3014.

[105] S. R. Salkuti, "Smart cities: Understanding policies, standards, applications and case studies," Int. J. Electr. Comput. Eng, vol. 11, no. 4, pp. 3137-3144, 2021, doi: 10.11591/ijece.vlli4.pp3137-3144.

[106] T. H. Tan, C. Y. Ooi, and M. N. Marsono, "An FPGA-based network system with service-uninterrupted remote functional update," Int. J. Electr. Comput. Eng, vol. 11, no. 4, pp. 3222-3228, 2021, doi: 10.11591/ijece.vlli4.pp3222-3228.

[107] B. Younes, F. Mohammed, M. Said, and M. El Bekkali, "5G uplink interference simulations, analysis and solutions: The case of pico cells dense deployment,"Int. J. Electr. Comput. Eng, vol. 11, no. 3, pp. 2245-2255, 2021, doi: 10.11591/ijece.vl li3.pp2245-2255.

[108] A. Abdulkareem, T. E. Somefun, O. K. Chinedum, and F. Agbetuyi, "Design and implementation of speech recognition system integrated with internet of things," Int. J. Electr. Comput. Eng, vol. 11, no. 2, pp. 1796-1803, 2021, doi: 10.11591/ijece.vlli2.ppl796-1803.

[109] J. C. Olivares-Rojas, E. Reyes-Archundia, J. A. Gutierrez-Gnecchi, I. Molina-Moreno, A. C. Tellez-Anguiano, and J. Cerda-Jacobo, "Smart metering system data analytics platform using multicore edge computing," Int. J. Reconfigurable Embed. Syst., vol. 10, no. 1, pp. 11-17, 2021, doi: 10.11591/ijres.vlO.il.ppl 1-17.

[110] I. Idrissi, M. Boukabous, M. Azizi, O. Moussaoui, and H. E. Fadili, "Toward a deep learning-based intrusion detection system for iot against botnet attacks," IAES Int. J. Artif. Intell, vol. 10,no. 1, pp. 110-120, 2021, doi: 10.11591/ijai.vlO.il.ppl 10-120.

[Ill] M. A. Al-Fayoumi, A. Odeh, I. Keshta, and A. Ahmad, "Techniques of medical image encryption taxonomy," Bull. Electr. Eng. Informatics, vol. 11, no. 4, pp. 1990-1997, 2022, doi: 10.11591/eei.vl li4.3850.

[112] T. Ghrib, M. Benmohammed, and P. S. Pandey, "Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques," Bull. Electr. Eng. Informatics, vol. 10, no. 2, pp. 950-961, 2021, doi: 10.11591/eei.vl0i2.2766.

[113] D. Dikii, S. Arustamov, and A. Grishentsev, "DoS attacks detection in MQTT networks," Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 1, pp. 601-608, 2021, doi: 10.11591/ijeecs.v21.il.pp601-608.

[114] R. I. Farhan, A. T. Maolood, and N. Hassan, "Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 3, pp. 1413-1418, 2020, doi- 10 11 591/iieecs v20 i3 nnl413-1418

[115] A. A. Abbood, Q. M. Shallal, and M. A. Fadhel, "Internet of things (IoT): A technology review, security issues, threats, and open challenges," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 3, pp. 1685-1692, 2020, doi: 10.11591/ijeecs.v20.i3.ppl685-1692.

[116] V. Sandeep, P. V Honagond, P. S. Pujari, S.-C. Kim, and S. R. Salkuti, "A comprehensive study on smart cities: Recent developments, challenges and opportunities," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 2, pp. 575-582, 2020, 10.11591/ijeecs.v20.i2.pp575-582.

[117] Q. Shallal, Z. Hussien, and A. A. Abbood, "Method to implement K-NN machine learningto classify data privacy in IoT environment," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 2, pp. 985-990, 2020, doi: 10.11591/ijeecs.v20.i2.pp985-990.

[118] H. M. Hoe and M. P. Abdullah, "Smart home appliances scheduling considering user comfort level," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 2, pp. 593-601, 2020, doi: 10.11591/ijeecs.v20.i2.pp593-601.

[119] M. M. Akawee, M. A. M. Al-Obaidi, H. M. T. Al-Hilfi, S. I. Jassim, and T. Sutikno, "An efficient hybrid model for secure transmission of data by using efficient data collection and dissemination (EDCD) algorithm based WSN," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 1, pp. 545-551, 2020, doi: 10.11591/ijeecs.v20.il.pp545-551.

[120] E. A. Tunggadewi, E. I. Agustin, and R. T. Yunardi, "A smart wearable device based on internet of things for the safety of children in online transportation," Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 2, pp. 100-108, 2020, doi: 10.11591/ijeecs.v22.i2.ppl00-108.

[121] R. A. Hamid and M. S. Croock, "A developed GPS trajectories data management system for predicting tourists' POI," Telkomnika (Telecommunication Comput. Electron. Control, vol. 18, no. 1, pp. 124-132, 2020, doi: 10.12928/TELKOMNIKA.V18I1.13006.

[122] S.-E. Chafi, Y. Balboul, M. Fattah, S. Mazer, M. El Bekkali, and B. Bernoussi, "Resource placement strategy optimization for IoT oriented monitoring application," Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 4, pp. 788-796, 2022, doi: 10.12928/TELKOMNIKA.v20i4.23762.

[123] F. Abdali-Mohammadi, M. N. Meqdad, and S. Kadry, "Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms," IAES Int. J. Artif. Intell., vol. 9, no. 4, pp. 766-771, 2020, doi: 10.11591/ijai.v9.i4.pp766-771.

[124] D. P. Kristiadi, F. Sudarto, E. F. Rahardja, N. R. Hafizh, C. Samuel, and H. L. H. S. Warnars, "Mobile cloud game in high performance computing environment," Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 4, pp. 1983-1989, 2020, doi: 10.12928/TELKOMNIKA.V18I4.14896.

[125] P. Manickam et al., "Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare," Biosensors, vol. 12, no. 8, 2022, doi: 10.3390/biosl2080562.

[126] Y. Hammoudi, I. Idrissi, M. Boukabous, Y. Zerguit, and H. Bouali, "Review on maintenance of photovoltaic systems based on deep learning and internet of things," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 1060-1072, 2022, doi: 10.11591/ijeecs.v26.i2.ppl060-1072.

[127] S. W. Shende, "Artificial intelligence and machine learning for internet of things," in Journal of Physics: Conference Series, 2021, vol. 1913, no. l,doi: 10.1088/1742-6596/1913/1/012151.

[128] A. Javali, A. Sahoo, and J. Gupta, "Machine Learning Algorithms in Smart Antenna and Arrays for Internet of Things Applications," in Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, 2021, pp. 297-301, doi: 10.1109/ICAIS50930.2021.9395928.

[129] N. D. Hettikankanamage and M. N. Halgamuge, "Digital Health or Internet of Things in Tele-Health: A Survey of Security Issues, Security Attacks, Sensors, Algorithms, Data Storage, Implementation Platforms, and Frameworks," Studies in Computational Intelligence, vol. 933. pp. 263-292, 2021, doi: 10.1007/978-981-15-9897-513.

[130] M. Jacoby and T. Uslander, "Digital twin and internet of things-Current standards landscape," Appl. Sci., vol. 10, no. 18, 2020, doi: 10.3390/APP10186519.

[131] M. Patel, B. Gohil, S. Chaudhary, and S. Garg, "Smart offload chain: a proposed architecture for blockchain assisted fog offloading in smart city," Int. J. Electr. Comput. Eng., vol. 12, no. 4, pp. 4137-4145, 2022, doi: 10.11591/ijece.vl2i4.pp4137-4145.

[132] K. Thavasimani and N. K. Srinath, "Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things-23 dataset," Int. J. Electr. Comput. Eng, vol. 12, no. 4, pp. 4031-4041, 2022, doi: 10.11591/ijece.vl2i4.pp4031-4041.

[133] O. Z. Salah, S. K. Selvaperumal, and R. Abdulla, "Accelerometer-based elderly fall detection system using edge artificial intelligence architecture,"Int. J. Electr. Comput. Eng,\o\. 12, no. 4, pp. 4430^1438, 2022, doi: 10.11591/ijece.vl2i4.pp4430-4438.

[134] W. C. Tan and M. S. Sidhu, "Review of RFID and IoT integration in supply chain management," Oper. Res. Perspect, vol. 9, p. 100229, 2022, doi: https://doi.Org/10.1016/j.orp.2022.100229.

[135] F.-J. Ferrandez-Pastor, J. Mora-Pascual, and D. Diaz-Lajara, "Agricultural traceability model based on IoT and Blockchain: Application in industrial hemp production," J. Ind. Inf.Integr., vol. 29, 2022, doi: 10.1016/j.jii.2022.100381.

[136] A. Chodorek, R. R. Chodorek, and P. Sitek, "Uav-based and webrtc-based open universal framework to monitor urban and industrial areas," Sensors, vol. 21, no. 12, 2021, doi: 10.3390/s21124061.

[137] M. A. Husnoo, A. Anwar, R. K. Chakrabortty, R. Doss, and M. J. Ryan, "Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey," IEEE Access, vol. 9, pp. 153276-153304, 2021, doi: 10.1109/ACCESS.2021.3124309.

[138] M. G. Labrador, A. Bordios, and W. Hou, "A cloud based 3-tier data security framework for industrial internet of things," Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 2, pp. 780-788, 2021, doi: 10.11591/ijeecs.v24.i2.pp794-802.

[139] D. N. C. Loong, S. Isaak, and Y. Yusof, "Machine vision based smart parking system using Internet of Things," Telkomnika (Telecommunication Comput. Electron. Control, vol. 17, no. 4, pp. 2098-2106, 2019, doi: 10.12928/TELKOMNIKA.vl7i4.12772.

[140] I. M. Murwantara and P. Yugopuspito, "An adaptive IoT architecture using combination of concept-drift and dynamic software product line engineering," Telkomnika (Telecommunication Comput. Electron. Control, vol. 19, no. 4, pp. 1226-1233, 2021, doi: 10.12928/TELKOMNIKA.vl9i4.19012.

[141] R. L. Kumar, F. Khan, S. Kadry, and S. Rho, "A Survey on blockchain for industrial Internet of Things: Blockchain for Internet of Things," Alexandria Eng. J., vol. 61, no. 8, pp. 6001-6022, 2022, doi: 10.1016/j.aej.2021.11.023. T1 A^~l /~^ T-1 -J-" A /~^ " T"l T"l /"^ O 1" J /~^ O ttT T1 Til I C J O "j- A A 1 -£* J-1 T J"

[142] G. Fortino, A. Guerrieri, P. Pace, C. Savagho, and G. Spezzano, lol Plattorms and Security: An Analysis ot the Leading Industrial/Commercial Solutions," Sensors, vol. 22, no. 6, 2022, doi: 10.3390/s22062196.

[143] S. Sagot, E. Ostrosi, and P. Lacom, "Computer-assisted culturalization process integration into product-web site design," J. Ind. Inf. Integr., vol. 26, 2022, doi: 10.1016/j.jii.2021.100252.

[144] T.-P. Hong, M.-J. Hu, T.-K Yin, and S.-L. Wang, "A Multi-Scale Convolutional Neural Network for Rotation-Invariant Recognition," Electron., vol. 11, no. 4, 2022, doi: 10.3390/electronicsl 1040661.

[145] B. D. Deebak, F. H. Memon, K. Dev, S. A. Khowaja, and N. M. F. Qureshi, "Al-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT," Ad Hoc Networks, vol. 125, 2022, doi: 10.1016/j.adhoc.2021.102740.

[146] Y. Chen, Y. A. Sambo, O. Onireti, and M. A. Imran, "A Survey on LPWAN-5G Integration: Main Challenges and Potential Solutions," IEEE Access, vol. 10, pp. 32132-32149, 2022, doi: 10.1109/ACCESS.2022.3160193.

[147] C. Xenofontos, I. Zografopoulos, C. Konstantinou, A. Jolfaei, M. K Khan, and K-K R. Choo, "Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies," IEEE Internet Things J., vol. 9, no. 1, pp. 199-221, 2022, doi: 10.1109/JIOT.2021.3079916.

[148] S. Ugwuanyi, G. Paul, and J. Irvine, "Survey of iot for developing countries: Performance analysis of lorawan and cellular nb-iot networks," Electron., vol. 10, no. 18, 2021, doi: 10.3390/electronicsl0182224.

[149] F. Arena and G. Pau, "An overview of big data analysis," Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4. pp. 1646-1653, 2020, doi: 10.11591/eei.v9i4.2359.

[150] S. R. Padupanambur, M. R. Ahmed, and B. P. Divakar, "Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks," Int. J. Electr. Comput. Eng., vol. 12, no. 4, pp. 4288^1301, 2022, doi: 10.11591/ijece.vl2i4.pp4288-4301.

[151] T. Sutikno, H. S. Purnama, A. Pamungkas, A. Fadlil, I. M. Alsofyani, and M. H. Jopri, "Internet of things-based photovoltaics parameter monitoring system using NodeMCU ESP8266," Int. J. Electr. Comput. Eng., vol. 11, no. 6, pp. 5578-5587, 2021, doi: 10.11591/ijece.vlli6.pp5578-5587.

[152] M. F. Falah et al, "Comparison of cloud computing providers for development of big data and internet of things application," Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 3. pp. 1723-1730, 2021, doi: 10.11591/ijeecs.v22.i3.ppl723-1730.

[153] C.-G Cgseong, J.-Y. Kim, and D.-J. Park, "Real-time object control system using open source platform," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 1, pp. 313-319, 2020, doi: 10.11591/ijeecs.v20.il.pp313-319.

[154] T. E. Somefun, A. Abdulkareem, C. O. Awosope, and O. Akanji, "Smart home comfort and energy conservation using internet of things," Telkomnika (Telecommunication Comput. Electron. Control, vol. 20, no. 2, pp. 357-365, 2022, doi: 10.12928/TELKOMNIKA.v20i2.18928.

[155] I. L. H. Alsammak, M. F. Alomari, I. S. Nasir, and W. H. Itwee, "A model for blockchain-based privacy-preserving for big data users on the internet of thing," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 974-988, 2022, doi: 10.11591/ijeecs.v26.i2.pp974-988.

[156] O. Al Shorman, B. Al Shorman, M. Al-Khassaweneh, and F. Alkahtani, "A review of internet of medical things (IoMT) - Based remote health monitoring through wearable sensors: A case study for diabetic patients," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 1, pp. 414^122, 2020, doi: 10.11591/ijeecs.v20.il.pp414-422.

[157] A. S. Oh, "Smart urban farming service model with IoT based open platform," Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 1, pp. 320-328, 2020, doi: 10.11591/ijeecs.v20.il.pp320-328.

[158] I. M. Nayyef and A. A. Hussien, "Intelligent power monitoring and control with wireless sensor network techniques," Indones. J. Electr. Eng. Comput. Sci., vol. 18, no. 2, pp. 1113-1122, 2020, doi: 10.11591/ijeecs.vl8.i2.pplll2-1020.

[159] K. Kommuri and V. R. Kolluru, "Implementation of modular MPPT algorithm for energy harvesting embedded and IoT applications," Int. J. Electr. Comput. Eng., vol. 11, no. 5, pp. 3660-3670, 2021, doi: 10.11591/ijece.vlli5.pp3660-3670.

[160] B. H. Hameed, A. Y. Taher, R. K. Ibrahim, A. H. Ali, and Y. A. Hussein, "Based on mesh sensor network: design and implementation of security monitoring system with Bluetooth technology," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 1781-1790, 2022, doi: 10.11591/ijeecs.v26.i3.ppl781-1790.

[161] R. Sudarmani, K. Venusamy, S. Sivaraman, P. Jayaraman, K. Suriyan, and M. Alagarsamy, "Machine to machine communication enabled internet of things: a review," Int. J. Reconfigurable Embed. Syst., vol. 11, no. 2, pp. 126-134, 2022, doi: 10.11591/ijres.vll.i2.ppl26-134.

[162] M. G Kibria and M. T. A. Seman, "Internet of things based automated agriculture system for irrigating soil," Bull. Electr. Eng. Informatics, vol. 11, no. 3, pp. 1752-1764, 2022, doi: 10.11591/eei.vl li3.3554.

[163] B. Edwin etal, "Smart agriculture monitoring system for outdoor and hydroponic environments," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, pp. 1679-1687, 2022, doi: 10.11591/ijeecs.v25.i3.ppl679-1687.

[164] I. Salehin et al., "IFSG: Intelligence agriculture crop-pest detection system using IoT automation system," Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 2, pp. 1091-1099, 2021, doi: 10.11591/ijeecs.v24.i2.ppl091-1099.

[165] K. Sekaran, M. N. Meqdad, P. Kumar, S. Rajan, and S. Kadry, "Smart agriculture management system using internet of things," Telkomnika (Telecommunication Comput. Electron. Control, vol. 18, no. 3, pp. 1275-1284, 2020, doi: 10.12928/TELKOMNIKA.vl8i3.14029.

[166] O Rahman et al, "Internet of things based electrocardiogram monitoring system using machine learning algorithm," Int. J. Electr. Comput. Eng, vol. 12, no. 4, pp. 3739-3751, 2022, doi: 10.11591/ijece.vl2i4.pp3739-3751.

[167] A. H. Shatti, H. A. Hasson, and L. A. Abdul-Rahaim, "Automation conditions of mobile base station shelter via cloud and IoT computing applications,"Int. J. Electr. Comput. Eng., vol. 11, no. 5, pp. 4550^1557, 2021, doi: 10.11591/ijece.vlli5.pp4550-4557.

[168] A. A. Ali, S. M. Saadi, T. M. Mahmood, and S. A. Mostafa, "A smart water grid network for water supply management systems," Bull. Electr. Eng. Informatics, vol. 11, no. 3, pp. 1706-1714, 2022, doi: 10.11591/eei.vl li3.3227.

[169] J. Geetha, S. Z. Kottur, R. Ganiga, D. S. Jayalakshmi, and T. Surabhi, "Analysis of rank-based latency aware fog scheduling using validating internet of things at large scales," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 3, pp. 1502-1511, 2022, doi: 10.11591/ijeecs.v26.i3.ppl502-1511.

[170] S.-E. Chafi, Y. Balboul, S. Mazer, M. Fattah, and M. E. Bekkali, "Resource placement strategy optimization for smart grid application using 5G wireless networks," Int. J. Electr. Comput. Eng., vol. 12, no. 4, pp. 3932-3942, 2022, doi: 10.11591/ijece.vl2i4.pp3932-3942.

[171] A. M. A. Jalil, R. Mohamad, N. M. Anas, M. Kassim, and S. I. Suliman, "Implementation of vehicle ventilation system using nodemcu ESP8266 for remote monitoring," Bull. Electr. Eng. Informatics, vol. 10, no. 1, pp. 327-336, 2021, doi: 10.11591/eei.vl0il.2669.

[172] T. Faisal, M. Awawdeh, and A. Bashir, "Design and development of intelligent waste bin system with advertisement solution," Bull. Electr. Eng. Informatics, vol. 10, no. 2, pp. 940-949, 2021, doi: 10.11591/eei.vl0i2.2753.

[173] M. U. H. Al Rasyid, S. Sukaridhoto, M. I. Dzulqornain, and A. Rifa'i, "Integration of IoT and chatbot for aquaculture with natural language processing," Telkomnika (Telecommunication Comput. Electron. Control, vol. 18, no. 2, pp. 640-648, 2020, doi: 10.12928/TELKOMNIKA.V18I2.14788.

[174] F. N. Shuhaimi, N. Jamil, and R. Hamzah, "Evaluations of internet of things-based personal smart farming system for residential apartments," Bull. Electr. Eng. Informatics, vol. 9, no. 6, pp. 2477-2483, 2020, doi: 10.11591/eei.v9i6.2496.

[175] P. Megantoro et al, "Instrumentation system for data acquisition and monitoring of hydroponic farming using ESP32 via Google Firebase,"Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 52-61, 2022, doi: 10.11591/ijeecs.v27.il.pp52-61.

[176] S. Janpla, C. Jewpanich, N. Tachpetpaiboon, W. Prongsanthia, and B. Jewpanich, "The development of smart flowerpot based on internet of things and mobile and web application technology," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 423-433, 2022, doi: 10.11591/ijeecs.v26.il.pp423-433.

[177] E. E. Hassan, L. L. Chung, M. F. Sulaima, N. Bahaman, and A. F. A. Kadir, "Smart irrigation system with photovoltaic supply," Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 29^11, 2022, doi: 10.11591/eei.vllil.3338.

[178] J. A. Hassan and B. H. Jasim, "Design and implementation of internet of things-based electrical monitoring system," Bull. Electr. Eng. Informatics, vol. 10, no. 6, pp. 3052-3063, 2021, doi: 10.11591/eei.vl0i6.3155.

[179] Q. Meng, Y. Hang, and X. Chen, "User roles in virtual community of crowdsourcing for innovation: A case study of Xiaomi MIUI in China," Teh. Vjesn., vol. 26, no. 5, pp. 1392-1399, 2019, doi: 10.17559/TV-20190627120336.

[180] P. Virdi, A. D. Kalro, and D. Sharma, "Consumer acceptance of social recommender systems in India," Online Inf. Rev., vol. 44, no. 3, pp. 723-744, 2020, doi: 10.1108/OIR-05-2018-0177.

[181] F. Zhao et al., "Computational Approaches to Detect Illicit Drug Ads and Find Vendor Communities Within Social Media Platforms," IEEE/ACM Trans. Comput. Biol. Bioinforma, vol. 19, no. 1, pp. 180-191, 2022, doi: 10.1109/TCBB.2020.2978476.

[182] D. He, Z. Yao, P. Tang, and Y. Ma, "Impacts of different interactions on viewers' sense of virtual community: an empirical study of live streaming platform," Behav. Inf. Technol, 2022, doi: 10.1080/0144929X.2022.2053884.

[183] C. Fathy and S. N. Saleh, "Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems," Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145075.

[184] M. A. Khan, I. M. Qureshi, I. Ullah, S. Khan, F. Khanzada, and F. Noor, "An efficient and provably secure certificateless blind signature scheme for flying ad-hoc network based on multi-access edge computing," Electron., vol. 9, no. 1, 2020, doi: 10.3390/electronics9010030.

[185] S. N. Swamy and S. R. Kota, "An empirical study on system level aspects of Internet of Things (IoT)," IEEE Access, vol. 8, pp. 188082-188134, 2020, doi: 10.1109/ACCESS.2020.3029847.

[186] W. Rafique, L. Qi, I. Yaqoob, M. Imran, R. U. Rasool, and W. Dou, "Complementing IoT Services through Software Defined Networking and Edge Computing: A Comprehensive Survey," IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1761-1804, 2020, doi: 10.1109/COMST.2020.2997475.

[187] A. Yazdinejad, G. Srivastava, R. M. Parizi, A. Dehghantanha, K.-K. R. Choo, and M. Aledhari, "Decentralized authentication of distributed patients in hospital networks using blockchain," IEEE J. Biomed. Heal. Informatics, vol. 24, no. 8, pp. 2146-2156, 2020, doi: 10.1109/JBHI.2020.2969648.

[188] M. Talebkhah, A. Sali, M. Marjani, M. Gordan, S. J. Hashim, and F. Z. Rokhani, "IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues," IEEE Access, vol. 9, pp. 55465-55484, 2021, doi: 10.1109/ACCESS.2021.3070905.

[189] I. Alam, S. Kumar, and P. K. Kashyap, "A seven-layered model architecture, network model, protocol stack, security, application, issues and challenges in internet of vehicle," Recent Patents Eng., vol. 15, no. 4, 2021, doi: 10.2174/1872212114999200719144002.

[190] M. S. Bali, K. Gupta, D. Koundal, A. Zaguia, S. Mahajan, and A. K. Pandit, "Smart architectural framework for symmetrical data offloading in IoT," Symmetry (Basel)., vol. 13, no. 10, 2021, doi: 10.3390/syml3101889.

[191] S. V Akram et al, "Role of wireless aided technologies in the solid waste management: A comprehensive review," Sustain., vol. 13, no. 23, 2021, doi: 10.3390/sul32313104.

[192] M. Hussein, Y. S. Mohammed, A. I. Galal, E. Abd-Elrahman, and M. Zorkany, "Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller," Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145106.

[193] I. L. Memon, S. Memon, J. A. Bhatti, A. S. Chan, and R. A. Memon, "FLA-IoT: Virtualization enabled architecture for heterogeneous systems in internet of things," Int. J. Adv. Comput. Sci. Appl, vol. 11, no. 4, pp. 360-366, 2020, doi: 10.14569/IJACSA.2020.0110450.

[194] W.-T. Sung and S.-J. Hsiao, "Utilizing the improved QPSO algorithm to build a WSN monitoring system," Comput. Mater. Contin., vol. 70, no. 2, pp. 3529-3548, 2022, doi: 10.32604/cmc.2022.020613.

[195] I. Kerrakchou, S. Chadli, Y. Ayachi, and M. Saber, "Modeling the impact of jamming attacks in the internet of things," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 1206-1215, 2022, doi: 10.11591/ijeecs.v26.i2.ppl206-1215.

[196] M. A. Obaidat, S. Obeidat, J. Hoist, A. A. Hayajneh, and J. Brown, "A comprehensive and systematic survey on the internet of things: Security and privacy challenges, security frameworks, enabling technologies, threats, vulnerabilities and countermeasures," Computers, vol. 9, no. 2, 2020, doi: 10.3390/computers9020044.

[197] S. N. Mahapatra, B. K. Singh, and V. Kumar, "A Survey on Secure Transmission in Internet of Things: Taxonomy, Recent Techniques, Research Requirements, and Challenges," Arab. J. Sci. Eng, vol. 45, no. 8, pp. 6211-6240, 2020, doi: 10.1007/sl3369-020-04461-2.

[198] U. Iqbal and A. H. Mir, "Secure and scalable access control protocol for IoT environment," Internet of Things (Netherlands), vol. 12, 2020, doi: 10.1016/j.iot.2020.100291.

[199] H. Cui, "Intelligent Coordination Distribution of the Whole Supply Chain Based on the Internet of Things," Complexity, vol. 2021, 2021, doi: 10.1155/2021/5555264.

[200] P. Franco, J. M. Martinez, Y.-C. Kim, and M. A. Ahmed, "IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes," IEEE Access, vol. 9, pp. 45325^15339, 2021, doi: 10.1109/ACCESS.2021.3067029.

[201] C. Peng, J. Chen, P. Vijayakumar, N. Kumar, and D. He, "Efficient Distributed Decryption Scheme for IoT Gateway-based Applications," ACM Trans. Internet Technol, vol. 21, no. 1, 2021, doi: 10.1145/3414475.

[202] T. Vaiyapuri, "Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment," Comput. Mater. Contin., vol. 68, no. 1, pp. 487-503, 2021, doi: 10.32604/cmc.2021.015998.

[203] F. C. Brandao, M. A. T. Lima, C. E. Pantoja, J. Zahn, and J. Viterbo, "Engineering approaches for programming agent-based iot objects using the resource management architecture," Sensors, vol. 21, no. 23, 2021, doi: 10.3390/s21238110.

[204] R. Mitra and R. Ganiga, "A novel approach to sensor implementation for healthcare systems using internet of things," Int. J. Electr. Comput. Eng, vol. 9, no. 6, pp. 5031-5045, 2019, doi: 10.11591/ijece.v9i6.pp5031-5045.

[205] N. Ismail, E. G. Yassine, and S. Abdelalim, "Towards a semantic web of things framework," lAESlnt. J. Artif Intel!., vol. 8, no. 4, pp. 443-450, 2019, doi: 10.11591/ijai.v8.i4.pp443-450.

[206] P. C. Siswipraptini, R. N. Aziza, I. Sangadji, and I. Indrianto, "The design of a smart home controller based on ADALINE," Telkomnika (Telecommunication Comput. Electron. Control, vol. 18, no. 4, pp. 2177-2185, 2020, doi: 10.12928/TELKOMNIKA.V18I4.14893.

[207] I. E. Salem, H. R. Abdulshaheed, and H. M. Gheni, "A secure telemedicine electronic platform based on lightweight cryptographic approach," Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 5, pp. 988-995, 2022, doi: 10.12928/TELKOMNIKA.v20i5.22662.

[208] K. Dusarlapudi, K. N. Raju, K. K. Kumar, K. Sudhakar, and C. S. Tiruvuri, "Design and prototyping of an accelerometer based parallel manipulator for endoscope position control," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 3, pp. 1320-1329, 2022, doi: 10.11591/ijeecs.v27.i3.ppl320-1329.

[209] S.-H. Kim, M.-H. Jeon, Y.-J. Jo, and C.-H. Oh, "Development of marine observation system using LPWA communication system for marine IoT service," Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 3, pp. 1556-1563, 2020, doi: 10.11591/ijeecs.vl9.i3.ppl556-1563.

[210] S. Metilda Florence, M. Uma, C. Fancy, and G. Saranya, "A study of remotely booking slot for vehicle using Internet of Things," Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 5392-5399, 2020, doi: 10.11591/IJECE.V10I5.PP5392-5399.

[211] R. G. A., S. P., and T. Z. Fadhil, "An efficient IoT based biomedical health monitoring and diagnosing system using myRIO," Telkomnika (Telecommunication Comput. Electron. Control, vol. 18, no. 6, pp. 3050-3057, 2020, doi: 10.12928/TELKOMNIKA.vl8i6.14375.

[212] H. F. Jassim, M. A. Tawfeeq, and S. M. Mahmoud, "Overlapped hierarchical clusters routing protocol for improving quality of service," Telkomnika (Telecommunication Comput. Electron. Control, vol. 19, no. 3, pp. 705-715, 2021, doi: 10.12928/TELKOMNIKA.vl9i3.18354.

[213] D. R. Prehanto, A. D. Indriyanti, and G S. Permadi, "Performance analysis routing protocol between RIPv2 and EIGRP with termination test on full mesh topology," Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 1, pp. 354-361, 2021, doi: 10.11591/ijeecs.v23.il.pp354-361.

[214] M. I. Nashiruddin, M. T. Baja Sihotang, and M. A. Murti, "Comparative study of low power wide area network based on internet of things for smart city deployment in Bandung city," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 1, pp. 425-439, 2022, doi: 10.11591/ijeecs.v25.il.pp425-439.

[215] C. L. Narayana, R. Singh, and A. Gehlot, "Performance evaluation of LoRa based sensor node and gateway architecture for oil pipeline management," Int. J. Electr. Comput. Eng., vol. 12, no. 1, pp. 974-982, 2022, doi: 10.11591/ijece.vl2il.pp974-982.

[216] Z. Y. M. Yusoff, M. K. Ishak, and L. A. B. Rahim, "A Java servlet based transaction broker for internet of things edge device communications," Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 488-497, 2022, doi: 10.11591/eei.vllil.3455.

[217] S. S. Patil and A. Biradar, "Novel authentication framework for securing communication in internet-of-things," Int. J. Electr. Comput. Eng, vol. 10, no. 1, pp. 1092-1100, 2020, doi: 10.11591/ijece.vl0il.ppl092-1100.

[218] A. Wani and S. Revathi, "Ransomware protection in IoT using software defined networking,"Int. J. Electr. Comput. Eng., vol. 10, no. 3, pp. 3166-3174, 2020, doi: 10.11591/ijece.vl0i3.pp3166-3175.

[219] M. U. H. Al Rasyid, M. H. Mubarrok, and J. A. N. Hasim, "Implementation of environmental monitoring based on kaa iot platform," Bull. Electr. Eng. Informatics, vol. 9, no. 6, pp. 2578-2587, 2020, doi: 10.11591/eei.v9i6.2578.

[220] M. El Ghmary, Y. Hmimz, T. Chanyour, and M. O. Cherkaoui Malki, "Time and resource constrained offloading with multi-task in a mobile edge computing node," Int. J. Electr. Comput. Eng., vol. 10, no. 4, pp. 3757-3766, 2020, doi: 10.11591/ijece.vl0i4.pp3757-3766.

[221] R. Atiqur, G Wu, and A. M. Liton, "Mobile edge computing for internet of things (IoT): Security and privacy issues," Indones. J. Electr. Eng. Comput. Sci., vol. 18, no. 3, pp. 1486-1493, 2020, doi: 10.11591/IJEECS.V18.I3.PP1486-1493.

[222] A. M. Alsmadi etal, "Fog computing scheduling algorithm for smart city," Int. J. Electr. Comput. Eng, vol. 11, no. 3, pp. 2219-2228, 2021, doi: 10.11591/ijece.vlli3.pp2219-2228.

[223] A. H. Shamman, H. A. Alasadi, H. A. Ameen, Z. I. Rasol, and H. M. Gheni, "Cost-effective resource and task scheduling in fog nodes,"Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 466-477, 2022, doi: 10.11591/ijeecs.v27.il.pp466-477.

[224] E. E. Abel, A. L. M. Shafie, and W. H. Chan, "Deployment of internet of things-based cloudlet-cloud for surveillance operations," IMS Int. J. Artiflntell, vol. 10, no. 1, pp. 24-34, 2021, doi: 10.11591/ijai.vl0.il.pp24-34.

[225] J. Li, R. Gopal, and A.N. Sigappi, "IoT in a museum for interactive experience design," Ann. Oper. Res., 2021, doi: 10.1007/s 10479-021-04419-z.

[226] F. Wang et al, "6g-enabled short-term forecasting for large-scale traffic flow in massive iot based on time-aware locality-sensitive hashing,"lEEElnternet Things.]., vol. 8, no. 7, pp. 5321-5331, 2021, doi: 10.1109/JIOT.2020.3037669.

[227] S. Hussain, U. Mahmud, and S. Yang, "Car e-Talk: An IoT-Enabled Cloud-Assisted Smart Fleet Maintenance System," IEEE Internet Things.]., vol. 8, no. 12, pp. 9484-9494, 2021, doi: 10.1109/JIOT.2020.2986342.

[228] O. H. Milani, S. A. Motamedi, S. Sharifian, and M. Nazari-heris, "Intelligent service selection in a multi-dimensional environment of cloud providers for internet of things stream data through cloudlets," Energies, vol. 14, no. 24, 2021, doi: 10.3390/enl4248601.

[229] K. Sadeghi, J. Kim, and J. Seo, "Packaging 4.0: The threshold of an intelligent approach," Compr. Rev. Food Sci. Food Saf, vol. 21, no. 3, pp. 2615-2638, 2022, doi: 10.1111/1541-4337.12932.

[230] J. Yang, X. Chen, H. Zou, D. Wang, Q. Xu, and L. Xie, "EfficientFi: Toward Large-Scale Lightweight WiFi Sensing via CSI Compression," lEEElnternet Things J., vol. 9, no. 15, pp. 13086-13095, 2022, doi: 10.1109/JIOT.2021.3139958.

[231] G L. Tortorella, A. Prashar, T. A. Saurin, F. S. Fogliatto, J. Antony, andG. C. Junior, "Impact of Industry 4.0 adoption on workload demands in contact centers," Hum. Factors Ergon. Manuf, vol. 32, no. 5, pp. 406^118, 2022, doi: 10.1002/hfm.20961.

[232] H. Hosseinian, H. Shahinzadeh, G B. Gharehpetian, Z. Azani, and M. Shaneh, "Blockchain outlook for deployment of IoT in distribution networks and smart homes," International Journal ofElectrical and Computer Engineering, vol. 10, no. 3. pp. 2787-2796, 2020, doi: 10.11591/ijece.vl0i3.pp2787-2796.

[233] A. Riansyah, S. Mulyono, and M. Roichani, "Applying fuzzy proportional integral derivative on internet of things for figs greenhouse," IMS International Journal of Artificial Intelligence, vol. 10, no. 3. pp. 536-544, 2021, doi: 10.11591/ijai.vl0.i3.pp536-544.

[234] Z. Anna and E. Vladimir, "State regulation of the IoT in the Russian Federation: Fundamentals and challenges," International Journal of Electrical and Computer Engineering, vol. 11, no. 5. pp. 4542^1549, 2021, doi: 10.11591/ijece.vlli5.pp4542-4549.

[235] D. J. Langley, J. van Doom, I. C. L. Ng, S. Stieglitz, A. Lazovik, and A. Boonstra, "The Internet of Everything: Smart things and their impact on business models," J. Bus. Res., vol. 122, pp. 853-863, 2021, doi: 10.1016/j.jbusres.2019.12.035.

[236] S. Sholla, R. N. Mir, and M. A. Chishti, "A neuro fuzzy system for incorporating ethics in the internet of things," J. Ambient Intell. Humaniz. Comput., vol. 12, no. 1, pp. 1487-1501, 2021, doi: 10.1007/sl2652-020-02217-2.

[237] A. Khan, A. Ahmad, M. Ahmed, J. Sessa, and M. Anisetti, "Authorization schemes for internet of things: requirements, weaknesses, future challenges and trends," Complex Intell. Syst., 2022, doi: 10.1007/s40747-022-00765-y.

[238] I. Gamal, H. Abdel-Galil, and A. Ghalwash, "Osmotic Message-Oriented Middleware for Internet of Things," Computers, vol. 11, no. 4, 2022, doi: 10.3390/computersl 1040056.

[239] R. Magdich, H. Jemal, and M. B. Ayed, "A resilient Trust Management framework towards trust related attacks in the Social Internet of Things," Comput. Commun, vol. 191, pp. 92-107, 2022, doi: 10.1016/j.comcom.2022.04.019.

[240] K A. Awan, I. U. Din, A. Almogren, and J. J. P. C. Rodrigues, "AutoTrust: A privacy-enhanced trust-based intrusion detection approach for internet of smart things," Futur. Gener. Comput. Syst., vol. 137, pp. 288-301, 2022, doi: 10.1016/j.future.2022.07.026.

[241] N. Tariq, M. Asim, F. A. Khan, T. Baker, U. Khalid, and A. Derhab, "A blockchain-based multi-mobile code-driven trust mechanism for detecting internal attacks in internet of things," Sensors (Switzerland), vol. 21, no. 1, pp. 1-26, 2021, doi: 10.3390/s21010023.

[242] M. Younan, E. H. Houssein, M. Elhoseny, and A. E.-M. Ali, "Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications," Peer] Comput. Sci., vol. 7, pp. 1-18, 2021, doi: 10.7717/PEERJ-CS.500.

[243] R. Gennari, A. Melonio, and M. Rizvi, "From children's ideas to prototypes for the internet of things: a case study of cross-generational end-user design," Behav. Inf. Technol, 2021, doi: 10.1080/0144929X.2021.1979654.

[244] M. Younan, M. Elhoseny, A. E.-M. A. Ali, and E. H. Houssein, "Data reduction model for balancing indexing and securing resources in the internet-of-things applications," IEEE Internet Things J., vol. 8, no. 7, pp. 5953-5972, 2021, doi: 10.1109/JIOT.2020.3035248.

[245] D. Wang, D. Zhong, and A. Souri, "Energy management solutions in the Internet ofThings applications: Technical analysis and new research directions," Cogn. Syst. Res., vol. 67, pp. 33-49, 2021, doi: 10.1016/j.cogsys.2020.12.009.

[246] S. Sholla, R. N. Mir, and M. A. Chishti, "A fuzzy logic-based method for incorporating ethics in the internet of things," Int. J. Ambient Comput. Intell, vol. 12, no. 3, pp. 98-122, 2021, doi: 10.4018/IJACI.2021070105.

[247] D. Xiang, X. Li, J. Gao, and X. Zhang, "A secure and efficient certificateless signature scheme for Internet of Things," Ad Hoc Networks, vol. 124, 2022, doi: 10.1016/j.adhoc.2021.102702.

[248] T. Rahaman, "Smart Things are Getting Smarter: An Introduction to the Internet of Behavior," Med. Ref. Serv. Q., vol. 41, no. 1, pp. 110-116, 2022, doi: 10.1080/02763869.2022.2021046.

[249] J. N. S. Rubi and P. R. L. Gondim, "Interoperable Internet of Medical Things platform for e-Health applications," Int. J. Distrib. Sens. Networks, vol. 16, no. 1, 2020, doi: 10.1177/1550147719889591.

[250] L. Diez, J. Choque, L. Sanchez, and L. Munoz, "Fostering IoT service replicability in interoperable urban ecosystems," IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3046286.

[251] B. Moons, M. Aernouts, V. Bracke, B. Volckaert, and J. Hoebeke, "Flint: Flows for the internet of things," Appl. Sci., vol. 11, no. 19, 2021, doi: 10.3390/appl 1199303.

[252] V. Lesi, Z. Jakovljevic, and M. Pajic, "IoT-Enabled Motion Control: Architectural Design Challenges and Solutions," IEEE Trans. Ind Informatics, pp. 1-11, 2022, doi: 10.1109/TII.2022.3202175.

[253] L.-G. Lemus-Zuniga, J. M. Felix, A. Fides-Valero, J.-V. Benlloch-Dualde, and A. Martinez-Millana, "A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards," Sensors, vol. 22, no. 4, 2022, doi: 10.3390/s22041646.

[254] G. Ortiz et al, "A microservice architecture for real-time IoT data processing: A reusable Web of things approach for smart ports," Comput. Stand. Interfaces, vol. 81, 2022, doi: 10.1016/j.csi.2021.103604.

[255] R. Wang, C. Gu, S. He, Z. Shi, and W. Meng, "An interoperable and flat Industrial Internet ofThings architecture for low latency data collection in manufacturing systems," J. Syst. Archil, vol. 129, 2022, doi: 10.1016/j.sysarc.2022.102631.

[256] S. Elhadi, L. Chhiba, N. Sael, and A. Marzak, "Guide to choosing internet of things protocols," Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 3, pp. 1557-1565, 2022, doi: 10.11591/ijeecs.v27.i3.ppl567-1575.

[257] R. Palanivelu and S. P.S.S., "Safety and security measurement in industrial environment based on smart IOT technology based augmented data recognizing scheme," Comput. Commun., vol. 150, pp. 777-787, 2020, doi: 10.1016/j.comcom.2019.12.013.

[258] S.-R. Oh and Y.-G. Kim, "AFaaS: Authorization framework as a service for Internet ofThings based on interoperable OAuth," Int. J. Distrib. Sens. Networks, vol. 16, no. 2, 2020, doi: 10.1177/1550147720906388.

[259] M. M. Martin-Lopo, J. Boal, and A. Sanchez-Miralles, "A literature review of IoT energy platforms aimed at end users," Comput. Networks, vol. 171, 2020, doi: 10.1016/j.comnet.2020.107101.

[260] R. Morabito and J. Jimenez, "IETF Protocol Suite for the Internet ofThings: Overview and Recent Advancements," IEEE Commun. Stand. Mag, vol. 4, no. 2, pp. 41-49, 2020, doi: 10.1109/MCOMSTD.001.1900014.

[261] B. Jasger and A. Mishra, "lot platform for seafood farmers and consumers," Sensors (Switzerland), vol. 20, no. 15, pp. 1-15, 2020, doi: 10.3390/s20154230.

[262] S. H. L. Liang et al, "An interoperable architecture for the internet of COVID-19 things (IOCT) using open geospatial standards- case study: Workplace reopening," Sensors (Switzerland), vol. 21, no. 1, pp. 1-33, 2021, doi: 10.3390/s21010050.

[263] A. Bhavana and A. N. Nandha Kumar, "ICS: Interoperable Communication System for Inter-Domain Routing in Internet-of-Things," Int. J. Adv. Comput. Sci. Appl, vol. 12, no. 5, pp. 268-275, 2021, doi: 10.14569/IJACSA.2021.0120533.

[264] D. Marsh-Hunn, S. Trilles, A. Gonzalez-Perez, J. Torres-Sospedra, and F. Ramos, "A Comparative Study in the Standardization of IoT Devices Using Geospatial Web Standards," IEEE Sens. J., vol. 21, no. 4, pp. 5512-5528, 2021, doi: 10.1109/JSEN.2020.3031315.

[265] V. Rajasekar, P. Jayapaul, S. Krishnamoorthi, M. Saracevic, M. Elhoseny, and M. Al-Akaidi, "Enhanced WSN Routing Protocol for Internet ofThings to Process Multimedia Big Data," Wirel. Pers. Commun., 2021, doi: 10.1007/sl 1277-021-08760-1.

[266] S. Lata, S. Mehfuz, and S. Urooj, "Secure and Reliable WSN for Internet ofThings: Challenges and Enabling Technologies," IEEE Access, vol. 9, pp. 161103-161128, 2021, doi: 10.1109/ACCESS.2021.3131367.

[267] M. Lu, G. Fu, N. B. Osman, and U. Konbr, "Green energy harvesting strategies on edge-based urban computing in sustainable internet of things," Sustain. CitiesSoc, vol. 75, 2021, doi: 10.1016/j.scs.2021.103349.

[268] G. Dec et al., "Role of Academics in Transferring Knowledge and Skills on Artificial Intelligence, Internet of Things and Edge Computing," Sensors, vol. 22, no. 7, 2022, doi: 10.3390/s22072496.

[269] Z. Din, D. I. Jambari, M. M. Yusof, and J. Yahaya, "Information systems security management for internet of things: Enabled smart cities conceptual framework," in SMARTGREENS 2020 - Proceedings of the 9th International Conference on Smart Cities and GreenICTSystems, 2020, pp. 44-51, doi: 10.5220/0009791700440051.

[270] R. Falcone and A. Sapienza, "Trust and autonomy for regulating the users' acceptance of IoT technologies," Intelligenza Artif, vol. 14, no. 1, pp. 19-32, 2020, doi: 10.3233/IA-190041.

[271] B. Attanasio, A. Mazayev, S. du Plessis, and N. Correia, "Cognitive load balancing approach for 6G MEC serving iot mashups," Mathematics, vol. 10, no. 1, 2022, doi: 10.3390/mathlOOlOlOl.

[272] C. C. Ferreira and F. Lind, "Supplier interfaces in digital transformation: an exploratory case study of a manufacturing firm and IoT suppliers," J. Bus. Ind. Mark, 2022, doi: 10.1108/JBIM-12-2021-0573.

[273] B. C. Fialho et al, "Development of a BIM and IoT-Based Smart Lighting Maintenance System Prototype for Universities' FM Sector," Buildings, vol. 12, no. 2, 2022, doi: 10.3390/buildingsl2020099.

[274] R. Saha et al, "DHACS: Smart Contract-Based Decentralized Hybrid Access Control for Industrial Internet-of-Things," IEEE Trans. Ind. Informatics, vol. 18, no. 5, pp. 3452-3461, 2022, doi: 10.1109/TII.2021.3108676.

[275] Z. Yu, S. A. R. Khan, M. Mathew, M. Umar, M. Hassan, and M. J. Sajid, "Identifying and analyzing the barriers of Internet-of-Things in sustainable supply chain through newly proposed spherical fuzzy geometric mean," Comput. Ind. Eng, vol. 169, 2022, doi: 10.1016/j.cie.2022.108227.

[276] T. D. Almeida, M. Costa Avalone, and D. C. Fettermann, "Building blocks for the development of an IoT business model," J. Strateg Manag, vol. 13, no. 1, pp. 15-32, 2020, doi: 10.1108/JSMA-07-2019-0130.

[277] M. Bures, M. Klima, V. Rechtberger, B. S. Ahmed, H. Hindy, and X. Bellekens, "Review of Specific Features and Challenges in the Current Internet ofThings Systems Impacting Their Security and Reliability," Advances in Intelligent Systems and Computing, vol. 1367 AISC. pp. 546-556, 2021, doi: 10.1007/978-3-030-72660-752.

[278] C.-H. Munoz-Flores and J. Olivella-Nadal, "Enablers and Inhibitors for IoT Implementation," International Series in Operations Research and Management Science, vol. 305. pp. 25^18, 2021, doi: 10.1007/978-3-030-70478-02.

[279] Z. Din, D. I. Jambari, M. M. Yusof, and J. Yahaya, "Information Systems Security Management for IoT Adoption in Smart Cities: A Review," Communications in Computer and Information Science, vol. 1475. pp. 66-92, 2021, doi: 10.1007/978-3-030-89170-1 4.

[280] L. Wang, A. A. Hamad, and V. Sakthivel, "IoT Assisted Machine Learning Model for Warehouse Management," J. Interconnect. Networks, 2021, doi: 10.1142/S0219265921430052.

[281] A. Infante-Moro, J. C. Infante-Moro, and J. Gallardo-Perez, "Key factors in the implementation of the internet of things in the hotel sixXac"Appl. Set, vol. 11, no. 7, 2021, doi: 10.3390/appl 1072924.

[282] S. Ahmed et at, "Towards supply chain visibility using internet of things: A dyadic analysis review," Sensors, vol. 21, no. 12, 2021, doi: 10.3390/s21124158.

[283] Z. Dou, Y. Sun, Z. Wu, T. Wang, S. Fan, and Y. Zhang, "The architecture of mass customization-social internet of things system: Current research profile," ISPRS Int. J. Geo-Information, vol. 10, no. 10, 2021, doi: 10.3390/ijgil0100653.

[284] N. Du and C. Chen, "Research on privacy protection system of RFID personal consumption data based on internet of things and cloud computing," Int. J. Inf. Comput. Secur., vol. 15, no. 4, pp. 328-342, 2021, doi: 10.1504/IJICS.2021.116930.

[285] M. El-Khoury and C. L. Arikan, "From the internet of things toward the internet of bodies: Ethical and legal considerations," Strateg. Chang., vol. 30, no. 3, pp. 307-314, 2021, doi: 10.1002/jsc.2411.

[286] A. Karale, "The Challenges of IoT Addressing Security, Ethics, Privacy, and Laws," Internet of Things (Netherlands), vol. 15, 2021, doi: 10.1016/j.iot.2021.100420.

[287] B. Esmaeilpour Ghouchani, S. Jodaki, M. Joudaki, A. Balali, and L. Rajabion, "A model for examining the role of the Internet of Things in the development of e-business," VINE J. Inf. Knowl. Manag Syst.,\o\. 50, no. 1, pp. 20-33, 2020, doi: 10.1108/VJIKMS-04-2019-0058.

[288] D. Zorbas, K. Abdelfadeel, P. Kotzanikolaou, and D. Pesch, "TS-LoRa: Time-slotted LoRaWAN for the Industrial Internet of Things," Comput. Commun., vol. 153, pp. 1-10, 2020, doi: 10.1016/j.comcom.2020.01.056.

[289] L. T. T. Nguyen et at, "BMDD: A novel approach for IoT platform (broker-less and microservice architecture, decentralized identity, and dynamic transmission messages),"Peer] Comput. Sci., vol. 8, 2022, doi: 10.7717/peerj-cs.950.

[290] X. Hao, W. Ren, Y. Fei, T. Zhu, and K. R. Choo, "A blockchain-based cross-domain and autonomous access control scheme for internet of things," IEEE Trans. Serv. Comput, p. 1, 2022, doi: 10.1109/TSC.2022.3179727.

[291] J.-S. Tsai, I.-H. Chuang, J.-J. Liu, Y.-H. Kuo, and W. Liao, "QoS-Aware Fog Service Orchestration for Industrial Internet of Things," IEEE Trans. Serv. Comput, vol. 15, no. 3, pp. 1265-1279, 2022, doi: 10.1109/TSC2020.2978472.

[292] N. Nithiyanandam, M. Rajesh, R. Sitharthan, D. Shanmuga Sundar, K. Vengatesan, and K. Madurakavi, "Optimization of Performance and Scalability Measures across Cloud Based IoT Applications with Efficient Scheduling Approach," Int. J. Wirel. Inf. Networks, 2022, doi: 10.1007/sl0776-022-00568-5.

[293] A. A. Ateya, M. Mahmoud, A. Zaghloul, N. F. Soliman, and A. Muthanna, "Empowering the Internet of Things Using Light Communication and Distributed Edge Computing,"Electron., vol. 11, no. 9, 2022, doi: 10.3390/electronicsll091511.

[294] J. Shah and B. Mishra, "IoT-enabled Low Power Environment Monitoring System for prediction of PM2.5," Pervasive Mob. Comput, vol. 67, 2020, doi: 10.1016/j.pmcj.2020.101175.

[295] G. Reggio, M. Leotta, M. Cerioli, R. Spalazzese, and F. Alkhabbas, "What are IoT systems for real? An experts' survey on software engineering aspects," Internet of Things (Netherlands), vol. 12, 2020, doi: 10.1016/j .iot.2020.100313.

[296] F. M. Ribeiro and C. A. Kamienski, "A Survey on Trustworthiness for the Internet of Things," IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3066457.

[297] J. Islam, T. Kumar, I. Kovacevic, and E. Harjula, "Resource-aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case," IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3102867.

[298] L. Tobarra, J. M. Haut, R. Hernandez, R. Pastor-Vargas, and A. Robles-Gomez, "Analyzing the users' acceptance of an IoT cloud platform using the UTAUT/TAM model," IEEE Access, vol. 9, pp. 150004-150020, 2021, doi: 10.1109/ACCESS.2021.3125497.

[299] C. Santana, L. Andrade, F. C. Delicato, and C. Prazeres, "Increasing the availability of IoT applications with reactive microservices," Serv. Oriented Comput. Appl, vol. 15, no. 2, pp. 109-126, 2021, doi: 10.1007/sl 1761-020-00308-8.

[300] K Demir, "A QoS-aware service discovery and selection mechanism for IoT environments," Sadhana -Acad. Proc. Eng. Sci., vol. 46, no. 4, 2021, doi: 10.1007/sl2046-021-01769-z.

[301] M. Vaezi et al, "Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G," IEEE Commun. Surv. Tutorials, vol. 24, no. 2, pp. 1117-1174, 2022, doi: 10.1109/COMST.2022.3151028.

[302] M. Cicioglu and A. Calhan, "Performance Analysis of Cross-Layer Design for Internet of Underwater Things," IEEE Sens. J., vol. 22, no. 15, pp. 15429-15434, 2022, doi: 10.1109/JSEN.2022.3187372.

[303] S. Vijayrao Shankhpal and B. Savadatti Hanumantha, "Design of a quality of service-aware fault-proof secure Q-learning-based Internet of Things kernel patch with multipath and multichannel capabilities," Int. J. Commun. Syst., vol. 35, no. 13, 2022, doi: 10.1002/dac.5236.

[304] M. B. Ortiz and S. Karapetrovic, "Developing Internet of Things-related ISO 10001 Hand Hygiene Privacy Codes in healthcare," TQM J., 2022, doi: 10.1108/TQM-03-2022-0081.

[305] Z. Waris, A. Jaleel, M. Shoaib, N. Nigar, and D. Abalo, "A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph," Math. Probl. Eng, vol. 2022, 2022, doi: 10.1155/2022/3278371.

[306] N. M. Pradhan, B. S. Chaudhari, and M. Zennaro, "6TiSCH Low Latency Autonomous Scheduling for Industrial Internet of Things," IEEE Access, vol. 10, pp. 71566-71575, 2022, doi: 10.1109/ACCESS.2022.3188862.

[307] D. D. Olatinwo, A. M. Abu-Mahfouz, and G P. Hancke, "Energy-Aware Hybrid MAC Protocol for IoT Enabled WBAN Systems," IEEESens. J., vol. 22, no. 3, pp. 2685-2699, 2022, doi: 10.1109/JSEN.2021.3133461.

[308] L. A. Saddik, B. A. Khalifa, and B. Fateh, "Evaluation quality of service for internet of things based on fuzzy logic: A smart home case study," Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 2, pp. 825-839, 2022, doi: 10.11591/ijeecs.v25.i2.pp825-839.

[309] T.-V. Nguyen, N.-N. Dao, V. Dat Tuong, W. Noh, and S. Cho, "User-Aware and Flexible Proactive Caching Using LSTM and Ensemble Learning in IoT-MEC Networks," IEEE Internet Things J., vol. 9, no. 5, pp. 3251-3269, 2022, doi: 10.1109/JIOT.2021.3097768.

[310] R. Bucea-Manea-tonis, L. Vasile, R. Stanescu, and A. Moanta, "Creating IoT-Enriched Learner-Centered Environments in Sports Science Higher Education during the Pandemic," Sustain., vol. 14, no. 7, 2022, doi: 10.3390/sul4074339.

[311] Q. W. Ahmed et al., "AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization," Electron., vol. 11, no. 13, 2022, doi: 10.3390/electronicsl 1132071.

[312] MobiDev, "Future of IoT Technology: 8 Trends for Businesses to Watch in 2022," 2022. https://www.iotforall.com/future-of-iot-technology-8-trends-for-businesses-to-watch-in-2022.

[313] L. Mumbi, "Internet of Things (IOT): What the Future Holds," BongoHive Technology & Innovation Hub, 2022. https://bongohive.co.zm/internet-of-things-iot-what-the-future-holds/.

[314] S. K Pillai, "What is The Effect of IoT on The Business Sector in 2022?," MQoS Technologies, 2022. https://medium.com/mqos technologies/what-is-the-effect-of-iot-on-the-business-sector-in-2022-608e794fcc55.

[315] M. Yazmin, C. M. Sreenath, K. R. Meagan, and S. Iyer, "Predicting Zero-Bin in the Semiconductor Manufacturing Industry: Machine Learning Algorithms," 2022.

[316] B. Adhi Santharm and U. Ramanathan, "Supply chain transparency for sustainability - an intervention-based research approach," Int. J. Oper. Prod. Manag., vol. 42, no. 7, pp. 995-1021, 2022, doi: 10.1108/IJOPM-l 1-2021-0684.

[317] V. Ramani, D. Ghosh, and M. S. Sodhi, "Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry," Omega (UnitedKingdom), vol. 113, 2022, doi: 10.1016/j.omega.2022.102720.

[318] J. Pridmore and A. Mols, "Personal choices and situated data: Privacy negotiations and the acceptance of household Intelligent Personal Assistants," Big Data Soc, vol. 7, no. 1, 2020, doi: 10.1177/2053951719891748.

[319] A. Papa, M. Mital, P. Pisano, and M. Del Giudice, "E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation," Technol Forecast. Soc. Change, vol. 153, 2020, doi: 10.1016/j.techfore.2018.02.018.

[320] S. Al-Sarawi, M. Anbar, R. Abdullah, and A. B. Al Hawari, "Internet of things market analysis forecasts, 2020-2030," in Proceedings of the World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2020, 2020, pp. 449-453, doi: 10.1109/WorldS450073.2020.9210375.

[321] S. V Khadonova, A. V Ufimtsev, and S. S. Dymkova, '"Digital smart airport' System based on innovative navigation and information technologies," 2020, doi: 10.1109/EMCTECH49634.2020.9261529.

[322] R. Matthew, J. Dutta, R. Maheswar, and K. Ahmed, "Intelligent Wearable Electronics: A New Paradigm in Smart Electronics," EAI/SpringerInnovations in Communication andComputing. pp. 169-197, 2021, doi: 10.1007/978-3-030-70183-3 7.

[323] G. Prasad, "Internet of things in the aerospace industry: Market analysis," in AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems, 2022, pp. 224-237.

[324] P.-S. Chiu, J.-W. Chang, M.-C. Lee, C.-H. Chen, and D.-S. Lee, "Enabling intelligent environment by the design of emotionally aware virtual assistant: A case of smart campus," IEEE Access, vol. 8, pp. 62032-62041, 2020, doi: 10.1109/ACCESS.2020.2984383.

[325] D. Caputo, L. Verderame, A. Ranieri, A. Merlo, and L. Caviglione, "Fine-hearing google home: Why silence will not protect your privacy," J. Wirel. Mob. Networks, Ubiquitous Comput. Dependable Appl., vol. 11, no. 1, pp. 35-53, 2020, doi: 10.22667/JOWUA.2020.03.31.035.

[326] A. Ermolina and V. Tiberius, "Voice-controlled intelligent personal assistants in health care: International delphi study," J. Med Internet Res., vol. 23, no. 4, 2021, doi: 10.2196/25312.

[327] D. Ferraris, D. Bastos, C. Fernandez-Gago, and F. El-Moussa, "A trust model for popular smart home devices," Int. J. Inf. Secur., vol. 20, no. 4, pp. 571-587, 2021, doi: 10.1007/sl0207-020-00519-2.

[328] D. Major, D. Y. Huang, M. Cherry, and N. Feamster, "Alexa, Who Am i Speaking To?: Understanding Users' Ability to Identify Third-Party Apps on Amazon Alexa," ACM Trans. Internet Technol, vol. 22, no. 1, 2022, doi: 10.1145/3446389.

[329] A. Cardoso, S. Kroehnert, R. Pinto, E. Fernandes, and I. Barros, "Integration of MEMS/Sensors in Fan-Out wafer-level packaging technology based system-in-package (WLSiP)," in Proceedings of the 2016 IEEE 18th Electronics Packaging Technology Conference, EPTC2016, 2017, pp. 801-807, doi: 10.1109/EPTC.2016.7861591.

[330] S. Burmaoglu, V. Trajkovik, T. L. Tutukalo, H. Yalcin, and B. Caulfield, "Evolution map of wearable technology patents for healthcare field," in Wearable Technology in Medicine andHealth Care, 2018, pp. 275-290.

[331] J. Wilden, A. Chandrakar, A. Ashok, and N. Prasad, "IoT based wearable smart insole," in 2017 Global Wireless Summit, GWS 2017, 2018, vol. 2018-January, pp. 186-192, doi: 10.1109/GWS.2017.8300466.

[332] T. Ahsan et al, "IoT Devices, User Authentication, and Data Management in a Secure, Validated Manner through the Blockchain System," Wirel. Commun. Mob. Comput, vol. 2022, 2022, doi: 10.1155/2022/8570064.

[333] X. Kong, Q. Chen, M. Hou, A. Rahim, K. Ma, and F. Xia, "RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern,"IEEE Trans. Veh. Technol, p. 1, 2022, doi: 10.1109/TVT.2022.3176243.

[334] D. Xia, L. Zheng, X. Cai, W. Liu, and D. Sun, "Urban Customized Bus Design for Private Car Commuters," IEEE Internet Things J., p. 1, 2022, doi: 10.1109/JIOT.2022.3181591.

[335] M. Aljarah, M. Shurman, and S. H. Alnabelsi, "Cooperative hierarchical based edge-computing approach for resources allocation of distributed mobile and IoT applications," International Journal of Electrical and Computer Engineering, vol. 10, no. 1. pp. 296-307, 2020, doi: 10.11591/ijece.vl0il.pp296-307.

[336] S. Agrawal, S. Bansal, and A. Sudha, "Accident detection, reporting and navigation over IoT," Int. J. Adv. Sci. Technol, vol. 29, no. 6, pp. 3213-3220, 2020.

[337] M. M. Salim, S. K. Singh, and J. H. Park, "Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks," Appl. Soft Comput, vol. 113, 2021, doi: 10.1016/j.asoc.2021.107859.

[338] F. Boumehrez, A. Hakim Sahour, and N. Doghmane, "Telehealth care enhancement using the internet of things technology," Bull. Electr. Eng Informatics, vol. 10, no. 5, pp. 2652-2660, 2021, doi: 10.11591/eei.vl0i5.2968.

[339] A. Chodorek, R. R. Chodorek, and A. Yastrebov, "Weather sensing in an urban environment with the use of a uav and webrtc-based platform: A pilot study," Sensors, vol. 21, no. 21, 2021, doi: 10.3390/s21217113.

[340] M. Gupta, N. Thakur, D. Bansal, G. Chaudhary, B. Davaasambuu, and Q. Hua, "CNN-LSTM Hybrid Real-Time IoT-Based Cognitive Approaches for ISLR with WebRTC: Auditory Impaired Assistive Technology," J. Healthc. Eng, vol. 2022, 2022, doi: 10.1155/2022/3978627.

[341] A. Chodorek, R. R. Chodorek, and A. Yastrebov, "The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform," Sensors, vol. 22, no. 4, 2022, doi: 10.3390/s22041578.

[342] G. Yascaribay, M. Huerta, M. Silva, and R. Clotet, "Performance Evaluation of Communication Systems Used for Internet of Things in Agriculture," Agric, vol. 12, no. 6, 2022, doi: 10.3390/agriculturel2060786.

[343] T. Green, P. Neumann, and K. Grey, "Mitigation of anti-competitive behaviour in telecommunication satellites and management of natural monopolies," in Proceedings of the International Astronautical Congress, IAC, 2018, vol. 2018-Octob.

[344] P. Z. Sotenga, K. Djouani, and A. M. Kurien, "Media Access Control in Large-Scale Internet of Things: A Review," IEEE Access, vol. 8, pp. 55834-55859, 2020, doi: 10.1109/ACCESS.2020.2982357.

[345] J. Ding, M. Nemati, C. Ranaweera, and J. Choi, "IoT connectivity technologies and applications: A survey," IEEE Access, vol. 8, pp. 67646-67673, 2020, doi: 10.1109/ACCESS.2020.2985932.

[346] D. Loghin et al, "The Disruptions of 5G on Data-Driven Technologies and Applications," IEEE Trans. Knowl. Data Eng., vol. 32, no. 6, pp. 1179-1198, 2020, doi: 10.1109/TKDE.2020.2967670.

[347] A. Minetto et al, "A testbed for gnss-based positioning and navigation technologies in smart cities: The hansel project," Smart Cities, vol. 3, no. 4, pp. 1219-1241, 2020, doi: 10.3390/smartcities3040060.

[348] M. H. M. Ghazali, K. Teoh, and W. Rahiman, "A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network," IEEEAccess, vol. 9, pp. 124817-124830, 2021, doi: 10.1109/ACCESS.2021.3110872.

[349] M. Malekzadeh, "Developing new connectivity architectures for local sensing and control IoT systems," Peer-to-Peer Netw. Appl, vol. 14, no. 2, pp. 609-626, 2021, doi: 10.1007/sl2083-020-01019-9.

[350] E. J. Khatib and R. Barco, "Optimization of 5G networks for smart logistics," Energies, vol. 14, no. 6, 2021, doi: 10.3390/enl4061758.

[351] A. Mahmood et al, "Industrial IoT in 5G-and-Beyond Networks: Vision, Architecture, and Design Trends," IEEE Trans. Ind. Informatics, vol. 18, no. 6, pp. 4122^1137, 2022, doi: 10.1109/TII.2021.3115697.

[352] A. H. Ali, M. N. Abbod, M. K. Khaleel, M. A. Mohammed, and T. Sutikno, "Large scale data analysis using MLlib," Telkomnika (Telecommunication Computing Electronics and Control), vol. 19, no. 5. pp. 1735-1746, 2021, doi: 10.12928/TELKOMNIKA.vl9i5.21059.

[353] Y.Wang, I. W.-H. Ho, Y.Chen, Y.Wang, and Y. Lin, "Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation," IEEE Internet Things.]., vol. 9, no. 16, pp. 14366-14374, 2022, doi: 10.1109/JIOT.2021.3078166.

[354] M. Babar, A. Din, O. Alzamzami, H. Karamti, A. Khan, and M. Nawaz, "A Bacterial Foraging Based Smart Offloading for IoT Sensors in Edge Computing," Comput. Electr. Eng., vol. 102, 2022, doi: 10.1016/j.compeleceng.2022.108123.

[355] A. O Khadidos, S. Shitharth, A. O Khadidos, K. Sangeetha, and K. H. Alyoubi, "Healthcare Data Security Using IoT Sensors Based on Random Hashing Mechanism," J. Sensors, vol. 2022, 2022, doi: 10.1155/2022/8457116.

[356] J. Wu, L. Sun, D. Peng, and S. Siuly, "A Micro Neural Network for Healthcare Sensor Data Stream Classification in Sustainable and Smart Cities," Comput. Intel! Neurosci., vol. 2022, p. 4270295, 2022, doi: 10.1155/2022/4270295.

[357] M. Al-Hawawreh, I. Elgendi, and K. Munasinghe, "An Online Model to Minimize Energy Consumption of IoT sensors in Smart Cites," IEEE Sens. J., p. 1, 2022, doi: 10.1109/JSEN.2022.3199590.

[358] M. R. A. Refaai, V. S. N. C. H. Dattu, H. S. Niranjana Murthy, P. Pramod Kumar, B. Kannadasan, and A. Diriba, "An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities," Adv. Mater. Sci. Eng, vol. 2022, 2022, doi: 10.1155/2022/7387346.

[359] R. Mohammedqasem, H. Mohammedqasim, and O. Ata, "Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network," Comput. Electr. Eng., vol. 100, 2022, doi: 10.1016/j.compeleceng.2022.107971.

[360] I. Tasic and M.-D. Cano, "Sparking Innovation in a Crisis: An IoT Sensor Location-Based Early Warning System for Pandemic Control,"Appl. Sci., vol. 12, no. 9, 2022, doi: 10.3390/appl2094407.

[361] R. S. Raghav, K. Thirugnanasambandam, V. Varadarajan, S. Vairavasundaram, and L. Ravi, "Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes," Big Data, vol. 10, no. 3, pp. 186-203, 2022, doi: 10.1089/big.2020.0203.

[362] C. Huang, W. Zhang, and L. Xue, "Virtual reality scene modeling in the context of Internet of Things," Alexandria Eng. J., vol. 61, no. 8, pp. 5949-5958, 2022, doi: 10.1016/j.aej.2021.11.022.

[363] C. Zhang, X. Yuan, Q. Zhang, G. Zhu, L. Cheng, and N. Zhang, "Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search" IEEE Internet Things.!., 2022, doi: 10.1109/JIOT.2022.3154605.

[364] K Yu, Z. Guo, Y. Shen, W. Wang, J. C.-W. Lin, and T. Sato, "Secure Artificial Intelligence of Things for Implicit Group Recommendations," IEEE Internet Things J., vol. 9, no. 4, pp. 2698-2707, 2022, doi: 10.1109/JIOT.2021.3079574.

[365] N. Ahmed and S. Misra, "Collaborative Flow-Identification Mechanism for Software-Defined Internet of Things," IEEE Internet Things J., vol. 9, no. 5, pp. 3457-3464, 2022, doi: 10.1109/JIOT.2021.3099822.

[366] T. Guo, K Yu, M. Aloqaily, and S. Wan, "Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT," Futur. Gener. Comput. Syst, vol. 128, pp. 381-394, 2022, doi: 10.1016/j.future.2021.09.044.

[367] C.-H. Lu and G.-Y. Fan, "Environment-Aware Dense Video Captioning for IoT-Enabled Edge Cameras," IEEE Internet Things J., vol. 9, no. 6, pp. 4554^1564, 2022, doi: 10.1109/JIOT.2021.3104289.

[368] W. Ullah et al, "Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data," Futur. Gener. Comput. Syst., vol. 129, pp. 286-297, 2022, doi: 10.1016/j.future.2021.10.033.

[369] S. Zhu, K Ota, and M. Dong, "Energy-Efficient Artificial Intelligence of Things With Intelligent Edge," IEEE Internet Things J., vol. 9, no. 10, pp. 7525-7532, 2022, doi: 10.1109/JIOT.2022.3143722.

[370] L. Jia, Z. Zhou, F. Xu, and H. Jin, "Cost-Efficient Continuous Edge Learning for Artificial Intelligence of Things," IEEE Internet Things J., vol. 9, no. 10, pp. 7325-7337, 2022, doi: 10.1109/JIOT.2021.3104089.

[371] C.-H. Lu and Y.-M. Zhou, "Direct Edge-to-Edge Many-to-Many Latent Feature Transfer Learning," IEEE Internet Things J., vol. 9, no. 12, pp. 10048-10060, 2022, doi: 10.1109/JIOT.2021.3117991.

[372] H. Shi and Q. Li, "Edge computing and the internet of things on agricultural green productivity," J. Supercomput., vol. 78, no. 12, pp. 14448-14470, 2022, doi: 10.1007/sll227-022-04463-x.

[373] T. Mai, H. Yao, J. Xu, N. Zhang, Q. Liu, and S. Guo, "Automatic Double-Auction Mechanism for Federated Learning Service Market in Internet of Things," IEEE Trans. Netw. Sci. Eng, 2022, doi: 10.1109/TNSE.2022.3170336.

[374] S. Liu et al, "CAQ: Towards Context-aware and Self-adaptive Deep Model Computation for AIoT Applications," IEEE Internet Things!, p. 1, 2022, doi: 10.1109/JIOT.2022.3176136.

[375] T. Hao, K Hwang, J. Zhan, Y. Li, and Y. Cao, "Scenario-based Al Benchmark Evaluation of Distributed Cloud/Edge Computing Systems,"IEEE Trans. Comput., p. 1, 2022, doi: 10.1109/TC2022.3176803.

[376] J. Kang et al, "Communication-Efficient and Cross-chain Empowered Federated Learning for Artificial Intelligence of Things," IEEE Trans. Netw. Sci. Eng, p. 1, 2022, doi: 10.1109/TNSE.2022.3178970.

[377] J. Chang, H. Ong, T. Wang, and H. Chen, "A Fully Automated Intelligent Medicine Dispensary System Based on AIoT," IEEE Internet Things!, p. 1, 2022, doi: 10.1109/JIOT.2022.3188552.

[378] B. Guo, Y. Liu, S. Liu, Z. Yu, andX. Zhou, "CrowdHMT: Crowd Intelligence with the Deep Fusion of Human, Machine, and IoT," IEEE Internet Things J., p. 1, 2022, doi: 10.1109/JIOT.2022.3194726.

[379] H.-T. Wu, "The internet-of-vehicle traffic condition system developed by artificial intelligence of things," J. Supercomput., vol. 78, no. 2, pp. 2665-2680, 2022, doi: 10.1007/sl 1227-021-03969-0.

[380] J.-W. Chang, "Enabling progressive system integration for AIoT and speech-based HCI through semantic-aware computing," J. Supercomput, vol. 78, no. 3, pp. 3288-3324, 2022, doi: 10.1007/sl 1227-021-03996-x.

[381] J. K Chiang, C.-L. Lin, Y.-F. Chiang, and Y. Su, "Optimization of the spectrum splitting and auction for 5th generation mobile networks to enhance quality of services for iot from the perspective of inclusive sharing economy," Electron., vol. 11, no. 1, 2022, doi: 10.3390/electronicsll010003.

[382] A. Darwish and A. E. Hassanien, "Fantasy Magical Life: Opportunities, Applications, and Challenges in Metaverses," J. Syst. Manag Sci., vol. 12, no. 2, pp. 411-436, 2022, doi: 10.33168/JSMS.2022.0222.

[383] L. Rydell, "Predictive Algorithms, Data Visualization Tools, and Artificial Neural Networks in the Retail Metaverse," Linguist. Philos. Investig, vol. 21, pp. 25-40, 2022, doi: 10.22381/lpi2120222.

[384] Y. Han et al., "A Dynamic Hierarchical Framework for IoT-assisted Digital Twin Synchronization in the Metaverse," IEEE Internet Things!, p. 1, 2022, doi: 10.1109/JIOT.2022.3201082.

[385] J. R. Bhat, S. A. AlQahtani, and M. Nekovee, "FinTech enablers, use cases, and role of future internet of things," J. King Saud Univ. - Comput. Inf.Sci., 2022, doi: 10.1016/j.jksuci.2022.08.033.

[386] Y. Deng, Z. Weng, and T. Zhang, "Metaverse-driven remote management solution for scene-based energy storage power stations," Evol. Intell, 2022, doi: 10.1007/sl2065-022-00769-0.

[387] B. Zhou, C. Xi, G. Li, and B. Yang, "Metaverse Application in Power Systems," Power Gener. Technol., vol. 43, no. 1, pp. 1-9, 2022, doi: 10.12096/j.2096-4528.pgt.21144.

[388] T. F. Tan et al., "Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges," Asia-Pacific J. Ophthalmol., vol. 11, no. 3, pp. 237-246, 2022, doi: 10.1097/APO0000000000000537.

[389] X. Zhang et al., "LLAKEP: A Low-Latency Authentication and Key Exchange Protocol for Energy Internet of Things in the Metaverse Era,"Mathematics, vol. 10, no. 14, 2022, doi: 10.3390/mathl0142545.