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The water crisis, global warming and climate changes have become recently prominent world issues. Saving and conserving water have become, therefore, an imperative for water resources’ sustainability. In this context, dramatic innovations have come to light, e.g., Precision Agriculture. Numerous research projects on this subject have been conducted, as illustrated not only by the huge number of research works in this topic, but also by the dozens of surveys dedicated to. Surveys regarding smart agriculture are of two categories, surveys on smart agriculture in general in which smart irrigation is a subtopic among others, and surveys dedicated to smart irrigation. Existing surveys, however, suffer from limitations including low paper numbers, linear description without structural classification, and missing key information. To fill this gap, we propose, in this paper, a survey on SI that classifies the large body of literature into categories in a structured way. To this end, we have employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to determine the inclusion or exclusion of articles. We have reviewed a total of 610 publications out of which 227 met the inclusion criteria. We have categorized the selected references into three conceptual clusters corresponding to: (1) 41.8% focusing on field measurements, based on IoT sensors, (2) 37% involving Remote Sensing (RS) and (3) 21.1% Artificial Intelligence (AI) methods. Each category is thoroughly described, including the inputs and outputs, along with a description of the various tasks employed in irrigation management. This systematic re-view is expected to serve as a useful reference for research on smart irrigation management methods.

Traditional networks become more difficult and complex due to their location-based communication policies. Researchers have proposed a new way of communication to overcome issues of traditional networks named Named Data Networking (NDN). In NDN, packets carry the name of the data instead of the source or destination IP addresses. Similarly, the Internet of Vehicles (IoVs) is a developing application of the Internet of Things (IoT) that has the potential to significantly increase network communication. To communicate helpful information with a particular group of vehicles, Named Internet of Vehicles (NIoVs) uses a broadcast pattern. However, there are several issues with security and packet duplication in NIoVs. In this paper, a solution related to the routing issue named broadcast storm within NIoV architecture is proposed. Denial of Service (DoS) attacks are also discussed in the context of NIoVs. A novel method called “Interest Flooding Attack Countermeasure and Controlled Packet Propagation (IFAC-CPP)” is proposed to counter both broadcast storms and DoS attacks. The first part of the proposed scheme deals with the problem of flooding attacks, while the second part deals with the issue of broadcast storms. This scheme is implemented in the communication environment of NIoVs in a highway scenario, with a focus on safety applications. Simulation results are compared with PP-FIRM and PP-FIRM with an attacker, demonstrating that the proposed scheme in this paper achieves better results in terms of network performance and efficiency.

As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demands limit their applications, leading to the emergence of Tiny Machine Learning (TinyML) as a more suitable solution for resource-constrained environments. TinyML is widely applied in areas such as smart homes, healthcare, and industrial automation. TinyML focuses on optimizing ML algorithms for small, low-power devices, enabling intelligent data processing directly on edge devices. This paper provides a comprehensive review of common challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints; it also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. On the other hand, this paper discusses TinyML’s applications in advancing cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a representative use case. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage, with a slight trade-off in accuracy. Additionally, the study includes a practical setup using the ESP32 microcontroller in the PlatformIO environment, which provides a hands-on assessment of TinyML’s application in cybersecurity for EVCI.

In this paper, we propose the design, operation, and implementation of an Internet of Things-based hybrid structural health monitoring. This innovative system leverages the capabilities of both fog and cloud layers in computing and monitoring. The system architecture consists of leaf nodes deployed on a target structure. These nodes, synchronously, collect acceleration signals from accelerometers attached directly to the structure and transmit the data to an on-site central node via a short-range communication protocol. At the fog layer, the central node, applies damage detection algorithms on the collected data. If a damage is detected, it forwards the acceleration signals to a cloud-based monitoring server using cellular internet connectivity, where more complex algorithms are used to identify and locate the damage. We provide detailed information about the design of the different system nodes, the implementation of damage detection algorithms, and the architecture of the monitoring server. To evaluate the effectiveness of the proposed system, several practical experiments were conducted. The results demonstrate that the hybrid system presented in this paper provides an efficient, reliable and cost-effective approach to damage detection and identification in civil infrastructures.

There is a growing demand for low-power network devices; therefore, enabling technologies for the Internet of Things (IoT) is significantly important. This paper proposed resource allocation by maximizing the harvested energy to substantially improve Energy Efficiency (EE) and regulate transmission power for the scheduled IoT devices. Energy Harvesting (EH) is a viable technology that enables long-term and self-sustainable operations for IoT devices. The Simultaneous Wireless Information and Power Transfer (SWIPT) has been proposed as a promising solution for maximizing EE while ensuring the quality of service of all IoT devices, where the ultra-low power devices harvest energy in Power Splitting (PS) mode. This paper applied the proposed Optimal Transmit Power and PS Ratio (OTPR) algorithm to maximize the EE for SWIPT based on the partial derivative of Lagrange dual decomposition methods. The algorithm jointly optimized the allocation of the channel, PS, and power control to solve the distributed non-convex and NP-hardness caused by co-channel interference. A novel training was proposed for Deep Neural Network (DNN) algorithms chain rules to minimize the loss function based on updating the parameters of the weights hidden layer and convergence training to achieve near-optimal performance and minimize unneeded label data. The simulation results showed that the DNN training for the chain rule provided a near-optimal performance EE with the shortest training time. This observation indicated that decreasing the loss function at every training optimizes the co-channel conditions for IoT devices by assigning the EH requirement to meet the minimum harvesting need.

The proliferation of Internet of Things (IoT) applications poses formidable challenges in managing data processing, privacy, and security. In response, technologies such as Fog Computing (FC), Blockchain (BC), and Federated Learning (FL) have emerged as promising solutions. Combining these technologies can broaden their scope, and impose novel challenges. This paper conducts a Systematic Literature Review (SLR) to investigate their integration within the IoT domain, systematically evaluating the current state-of-the-art by analyzing 40 papers against 38 extraction criteria, encompassing technical characteristics specific to FC, BC, FL, or their integration. The findings offer insights into the advantages, challenges, opportunities, and limitations of this integration, addressing data processing, privacy, and security concerns in IoT. By filling a research gap and directly examining FC, BC, and FL interoperability across architectural layers, this study contributes to knowledge expansion in the field. This paper proposes a novel framework for implementing FL and BC within FC environments for IoT applications, alongside a comprehensive synthesis of existing literature, distinguishing it from previous research efforts. Furthermore, it offers valuable insights into the current landscape, identifies research needs, and proposes future research directions. The framework and literature synthesis provided allow readers to access customized information on FC-BC-FL integration, aiding in designing and implementing robust IoT solutions.

The Internet of Things (IoT) represents a dynamic infrastructure, leveraging sensing and network communication technology to establish ubiquitous connectivity among people, machines, and objects. Due to its end devices’ limited computing resources and storage space, it is not feasible to merely transpose traditional internet security technologies directly to IoT endpoints. Maintaining security while concurrently ensuring performance is a particularly challenging endeavor. This paper provides a review of key agreements and authentication protocols pivotal to the security of IoT. First, this survey discusses the applications that need authentication and key agreement to strengthen their security and current research on these application fields. Subsequently, this paper engages in an in-depth exploration of the phase involved in the scheme of authentication and key agreement, including an examination of the cryptographic techniques employed within these processes. This survey also thoroughly studies the scheme’s security services, potential attacks, formal analysis and informal analysis to ensure resilience against such threats. This study aims to provide a profound understanding of the recent research on authentication and key agreement in IoT applications. It strives to contribute towards strengthening security systems for IoT applications, ensuring their sustainability in the face of evolving threats.

Cyber Physical System (CPS) and Internet of Things (IoT) have attracted attention as a promising technology for the future society. Although the 6th generation mobile system is expected to work as the CPS/IoT platform, the specific technical requirements are not studied thoroughly. In this paper, we take application driven approach focusing on three applications, eHealth, smart manufacturing, and smart city, to identify mandatory technical requirements for the 6G mobile core network architecture to realize CPS/IoT systems. Specifically, this paper gives the following three contributions. First, we investigate several use cases in the applications listed above and list up the requirements for the core network, i.e., the massive connectivity, the flexible SLA, the ambient IoT device accommodation, the wireless sensing function, and the AI-aware data sharing. Second, we identify the issues to satisfy those requirements in the current 5G technologies. Lastly, we pick up the 6G architectures proposed currently and discuss their pros and cons based on the requirements and issues pointed out.

To support dramatically increasing services from internet of thing (IoT) devices with the sporadic and fluctuated generation of short packet traffic, this paper investigates joint dynamic time division duplexing (TDD) and radio resource control (RRC) connection management in a single-cell massive IoT network. Specifically, under the grant-free transmission incurring packet collision, this study models the factors affecting the time resource utilization (TRU) and energy consumption of IoT devices as a comprehensive system utility and further formulates the problem as a decision-making process aiming for balancing the long-term average TRU and energy consumption. To address the formulated problem, based on the deep reinforcement learning framework, this paper designs an experience-driven joint dynamic TDD and RRC connection management scheme that intelligently i) determines the TDD configuration based on the most recent downlink (DL)/uplink (UL) traffic demands and ii) adjusts the RRC state of each IoT device to control the maximum number of transmitting IoT devices. Finally, trace-driven simulation results demonstrate that the proposed scheme outperforms existing benchmarks, such as Static TDD and Dynamic TDD, in terms of transmission success ratio difference (TSRD) (up to 89% reduction), time resource utilization (TRU) (up to $17\times $ increase), and energy consumption (up to 70% reduction) of IoT devices.

Power outages can severely affect individuals, businesses, and communities, leading to disruptions, economic losses, and safety risks. The existing power recovery strategies often fail to adequately address the challenges associated with such outages. These challenges encompass a range of complexities, including resource allocation disparities, efficient prosumer integration, energy demand variability, and isolated generators. This paper presents a microgrid-centric power recovery strategy that leverages IoT, blockchain, smart contracts, and optimisation techniques for peer-to-peer energy sharing within the microgrid. The proposed strategy comprehensively addresses the challenges associated with the existing power recovery strategies. The paper outlines the system architecture for IoT and blockchain-enabled microgrids, discusses the mathematical modelling for energy sharing, and explores cost-optimal power restoration strategies. An incentive mechanism motivates prosumers to support restoration strategies during outages. Furthermore, the paper describes a blockchain smart contract facilitating peer-to-peer energy exchange in regions affected by power outages. This approach can mitigate the disruptive impact of power outages by providing reliable and community-centric power recovery solutions. Through validation with real-world data from our university’s distribution grid test bed, Mean Time To Recover (MTTR) analysis and performance evaluations using the Hyperledger Caliper benchmark tool, this paper demonstrates its feasibility and effectiveness, paving the way for enhanced power recovery strategies and increased resilience in the face of energy disruptions.

Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to connect vast numbers of devices to gather and distribute vital information. Despite individual devices having low energy consumption, the cumulative demand results in significant energy usage. Consequently, the concept of ultra-low-power tags gains appeal. Such tags communicate by reflecting rather than generating the radio frequency (RF) signals by themselves. Thus, these backscatter tags can be low-cost and battery-free. The RF signals can be ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV) signals, or the system can generate them externally. Backscatter channel characteristics are different from conventional point-to-point or cooperative relay channels. These systems are also affected by a strong interference link between the RF source and the tag besides the direct and backscattering links, making signal detection challenging. This paper provides an overview of the fundamentals, challenges, and ongoing research in signal detection for AmBC networks. It delves into various detection methods, discussing their advantages and drawbacks. The paper’s emphasis on signal detection sets it apart and positions it as a valuable resource for IoT and wireless communication professionals and researchers.

This paper aims to provide an automatic analysis of music emotion, enhancing users’ ability to perceive and comprehend the emotional nuances conveyed in music intuitively. It first reviews the limitations of traditional music emotion analysis methods, and highlights the transformative potential of Internet of Things (IoT) technology in addressing these limitations. Subsequently, the paper employs the Long Short-Term Memory (LSTM) network to model time series data in music, integrating it with the Sequence-to-Sequence (STS) framework to construct an advanced music emotion analysis model. Finally, several machine learning algorithms are adopted to train and evaluate the system. The findings underscore the efficacy of the music emotion analysis model based on the LSTM network fused with STS, demonstrating notable success in the task of music emotion prediction. Specifically, the fusion model achieves an average absolute error of Arousal value of 0.921, a root mean square error (RMSE) of 0.534, and an R square of 0.498. The average absolute error in the Valence value is 0.902, the RMSE is 0.575, and the R square is 0.478. This paper holds significant implications for a deeper understanding of music emotion and provides guidance for music recommendation and creation.

The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed. Through empirical investigation, the report highlights greater efficiency, transparency, and security as significant benefits of BIoT architecture. A cost analysis is also conducted, showing that the system is cost-effective. The paper also proposes a mathematical formula for the calculation of infrastructure cost. It also looks at the problems of integrating BIoT in agricultural contexts, such as technological complications and the need for stakeholder participation. The findings indicate that the IoT can potentially revolutionize agricultural practices drastically, but its practical implementation would need careful consideration of technological, economic, and societal considerations. Empirical analysis is conducted to analyze the latency, throughput, and transaction scalability. The paper concludes with research suggestions, emphasizing the significance of interdisciplinary collaboration and the consequences of BIoT deployment in agriculture.

As one of the important components of internet of things (IOT) systems, radio frequency identification (RFID) tags need to be improved in terms of power consumption, size reduction and coding capacity. In conventional methods, designers focus on optimizing resonators, but by focusing on the use of phase coding, a new method can be presented that results in a tag with smaller dimensions and more coding capacity. Hybrid encoding for RFID is based on the phase coding by changing the position of the resonator in the chipless RFID tag. In this paper a C-shaped resonator is proposed to design a chipless tag on the Rogers RT/duroid 5880 laminates. This method proposes different phase values in the resonance frequency as separate codes. The difference between the phases ( $\Phi$ ) of the scattering parameters S11 and S22 is measured to show the effect of position varying of the proposed C-shaped resonator. With the three different values of $\Phi $ at the resonance frequency, the proposed RFID tag creates codes 0, 1 and 2, in comparison with the conventional structures that create codes 0 and 1 only, based on the presence or absence of a transition zero at the resonance frequency. The proposed structure was designed, fabricated and measured and measurement results validate this method. Accordingly, an RFID tag with five C-shaped resonators is proposed, where the simulation results of its $3^{5}=243$ different states are presented in this paper.

Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algorithms need to be more compact yet suitable for embedded devices with limited resources and remain acceptable accuracy. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for IoT devices. This paper introduces the novel Chessboard-based Automatic Modulation Classification (CAMC) algorithm, which has dramatically high accuracy. Test results reveal that CAMC achieves 99%* accuracy under a 3dB SNR condition and 100% above 5dB SNR. Meanwhile, this algorithm is scalable and demands less computing power. It offers better accuracy results compared to state-of-the-art AMC algorithms, classifying mainstream modulations in IoT devices like BPSK, QPSK, 8PSK, and 16QAM, but requires less computing power than existing algorithms. Additionally, CAMC is hardware-friendly due to its inherent parallelism and scalability. The novelty of this paper is to classify 4 different modulations in a low-computation-loading required and hardware-friendly way and achieve a high accuracy of over 99%* above SNR of 3dB. (* Accuracy that most of the time could reach)

In smart homes, the management of energy is gaining huge significance among researchers in recent times. This paper presents a system for predicting power utilization and scheduling household appliances in smart homes. The system utilizes a combination of Grey Wolf optimization (GWO), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to improve energy management. The GWO algorithm is used to enhance the performance of the CNN-LSTM model. GWO is an optimization algorithm inspired by the hunting behaviour of grey wolves. It helps in finding optimal solutions for complex problems by mimicking the social hierarchy and hunting mechanisms of wolves. The fusion of CNN and LSTM serves as a pattern finding strategy for energy management. CNN is effective in extracting spatial features from data, while LSTM can capture temporal dependencies. By combining these two approaches, the model can analyze energy consumption patterns and make accurate predictions. To evaluate the performance of the proposed model, the paper uses three error metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The reported values of RMSE, MSE, and MAE are 0.6213, 0.3860, and 0.2808, respectively. These metrics indicate the accuracy of the model’s predictions, with lower values indicating better performance. Furthermore, this paper compares the proposed approach with the existing baseline models to access its superiority. According to the results, the proposed model outperforms the existing approaches in terms of prediction accuracy, as it achieves lower errors, compared to the baseline models. In summary, the proposed GWO-based CNN-LSTM network demonstrates improved prediction accuracy compared to the existing approaches, as indicated by the evolution metrics.

Technology’s fast growth has profoundly impacted myriad areas, including healthcare. Implementing 5G networks offering high-speed and low-latency communication capabilities is one of the most promising technical developments. Parallel to this, artificial intelligence (AI) has become a robust data analysis and decision-making tool. This paper examines how 5G and AI are combined in the context of intelligent healthcare systems. T5G green communication systems must overcome several challenges to satisfy the need for more user capacity, faster network speeds, cheaper pricing, and less resource use. By applying 5G standards, data rates, and device dependability for Industry 4.0 applications may be significantly increased. Advanced security and decreased unauthenticated assaults from various platforms are also covered in the paper. An outline of prospective new technologies and security improvements was provided to safeguard 5G-based intelligent healthcare networks. This paper identifies several research issues and potential future directions for secure 5G-based smart healthcare. This article discusses Industry 4.0, 5G standards, and new research in future wireless communications to explore current research concerns related to 5G technology. A brand-new architecture is also suggested in the paper for Industry 4.0 and 5G-enabled intelligent healthcare systems. Our method achieves an average improvement of 25% compared to existing techniques. Furthermore, we observe a 20% reduction in computational costs, making it a more efficient solution for real-world applications. These findings underscore the practical value of our research and pave the way for improved healthcare applications in 5G communication.

Today we are living in a society where an ever increasing number of devices or things are being connected to the Internet. Many of these devices are battery powered with the long term success of the business case justifying the deployment of these devices, dependent upon the ability of these devices to operate autonomously for extended periods without intervention to replace batteries. Utility companies delivering water and gas are seeking 10 year plus lifetimes from devices without a truck roll to replace batteries. The current industry understanding of the 5G technologies, NB-IoT and eMTC, is NB-IoT offers lower power consumption but there is little evidence for that belief. This paper uses standard load models to systematically investigate the energy demands of the two different radio access technologies under realistic usage patterns. The paper also investigates some key factors influencing energy consumption and whether a device could operate from an ideal 5 Wh battery for 10 years. In contributing to the field of knowledge in this area, we set out to compare 3GPP IoT radio access technologies using techniques aligned with existing 3GPP energy consumption evaluation methodologies. In adopting that approach, contrary to established industry opinions and messaging, we found when operating as an eMTC device, our radio module consumes less power than when it was operating as an NB-IoT device. Irrespective of it’s operating mode, our radio module was unable to achieve a 10 year battery life from an ideal 5 Wh battery using a typical IoT traffic model. Most interestingly, we observed minimal change in energy consumption whether the payload size was 1 byte or 1400 bytes.

Apache Kafka has become a popular solution for managing networked data streaming in a variety of applications, from industrial to general purpose. This paper systematically surveys the research literature in this field by carefully classifying it into key macro areas, namely algorithms, networks, data, cyber-physical systems, and security. Through this meticulous classification, the paper aims to identify and analyze the optimization aspects relevant to each area, drawing upon practical applications as the basis for analysis. In this respect, the paper synthesizes and consolidates existing knowledge, saving researchers valuable time and effort in searching for relevant information across multiple sources. The tangible benefits of this survey paper include providing a consolidated knowledge base about research-intensive Apache Kafka topics, highlighting practical insights and novel approaches, pointing up cross-domain applications, identifying related research challenges, and serving as a trusted reference for the Apache Kafka community.

The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.

This paper presents a compact broadband stepped bow-tie antenna for ambient RF energy harvesting (RFEH). The proposed antenna consists of stepped shapes for wide bandwidth, making it possible to investigate ambient RF energy in entire cellular bands. In addition, the antenna is composed of a bow-tie shape to achieve compactness for internet of things (IoT) sensors. We explain the design steps taken to achieve this wide bandwidth, analyzing the surface current distributions with respect to the number of stepped shapes through an EM simulation. In addition, we cut the broadband antenna in half and integrated it with the RFEH circuit for compactness, causing impedance matching to deteriorate. The novel modified bow-tie shaped half antenna improved impedance matching, which is analyzed with the surface current distribution. We found that the proposed antenna has a fractional bandwidth of 125% of $S_{11} < -10$ dB, ranging from 0.85 GHz to 3.66 GHz with a compact dimension of 68 mm $\times107$ mm ( $0.19\,\,\lambda _{0} \times 0.30\,\,\lambda _{0}$ ). With the proposed antenna, we investigated ambient RF energy at several outdoor sites and chose the feasible frequency bands (0.9 GHz and 1.8 GHz). For the bands, the RFEH circuit was designed consisting of a rectifier and power management unit (PMU), and integrated into the proposed antenna. In one site, we experimented and verified the RFEH with the antenna. The broadband and compact antenna in this paper can contribute to implementing the RFEH prototype for the capability to be used as a power source for IoT sensors.

At present, the transportation industry is developing rapidly. Studying intelligent highway systems in the Internet of Things (IoT) context is of practical significance. This paper aims to promote the comprehensive integration of modern information technology with transportation facility management and operation management. Firstly, the fundamental nature and laws of IoT and intelligent transportation are studied. Based on the current situation, the feasibility of developing intelligent transportation based on the IoT technology of the highway is analyzed. Secondly, the design requirements of the highway Intelligent Transportation System (ITS) are analyzed. Then, the overall architecture of the highway ITS is designed by comprehensively using various advanced information, communication, and control technologies guided by demand analysis and relying on the technology platform of the IoT. It is identified as three layers: a perceptual layer, a transport layer, and an application layer. Finally, the main application function design of the highway ITS is carried out. The results show that: 1) the efficiency and effectiveness of the maintenance management system in dealing with emergencies before equipment failure is much higher than that of the traditional highway management system; 2) after using the updated system, user experience satisfaction and executive experience satisfaction have improved. It is more ideal than traditional highway traffic processing methods; and 3) expectation of the public for the innovative system designed here is 4.8. It reveals that the proposed intelligent transportation system meets the expectation of the majority of the public for highway traffic management. The purpose of this paper is to promote the development of transportation informatization and intelligence, improve the efficiency of the transportation system, and enhance the safety of transportation through IoT information technology.

The whole process of tobacco production is composed of many components, in which their operation and administration are currently independent. It is required to deploy smart manufacturing workflow for the whole production process, in order to realize centralized effective global scheduling. This requires an advanced administration control platform that has strong abilities of multisource data integration and automatic decision support. To bridge such research gap, this paper designs an optimal scheduling control system for smart manufacturing processes of tobacco industry. First of all, this work discusses major characteristics of future-generation production control patterns in intelligent tobacco factories (ITF). Then, a five-layer architecture for optimal scheduling control of ITF is proposed, which contains Internet-of-Things layer, centralized control layer, model layer, platform layer and operation layer. In addition, a production scheduling optimization strategy is also developed for the proposed system to serve as the software algorithm that drives the running of whole smart manufacturing processes. Finally, this paper presents a comparative analysis of the proposed system’s transformation in a cigarette factory. Naturally, the effectiveness of the proposed production optimization scheduling strategy is verified through simulation.

Machine learning (ML) and the internet of things (IoT) are among the most booming research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early warning systems (EWSs), etc.; or it could be called “Smart x” systems are implemented using ML and IoT. Those systems will alter how various world entities communicate with one another. This paper spots light on the significant roles of the IoT in SS. Also, it focuses on the importance of ML in IoT-based SS. Besides, an overview of smartness and IoT is presented. Then, this paper offers ML benchmarking along with a taxonomy that categorizes the ML models into linear and non-linear ones depending on the problem type (classification or regression). Afterward, the commonly utilized evaluation metrics are provided. In addition, this paper considers the trust techniques used for mitigating different security aspects in IoT networks, which play a crucial part in regulating the new era of communication. Moreover, two case studies devoting ML for IoT-based SS, namely IoT-based SC and IoT-based EWS, are considered for data collection and manipulation with guided research directions. Finally, the paper presents effective recommendations of ML’s significant roles in SC and earthquake EWS for interested scholars.

As the number of smart connected devices increases day by day, a massive amount of tasks are generated by various types of Internet of Things (IoT) devices. Intelligent edge computing is a promising enabler in next-generation wireless networks to execute these tasks on proximate edge servers instead of smart devices. Additionally, regarding the execution of tasks in edge servers, smart devices could provide a low-latency environment to the end users. Within this paper, an artificial intelligence (AI)-empowered fast task execution method in heterogeneous IoT applications is proposed to reduce decision latency by taking into account different system parameters such as the execution deadline of the task, battery level of devices, channel conditions between mobile devices and edge servers, and edge server capacity. In edge computing scenarios, the number of task requests, resource constraints of edge servers, mobility of connected devices, and energy consumption are the main performance considerations. In this paper, the AI-empowered fast task decision method is proposed to solve the multi-device edge computing task execution problem by formulating it as a multi-class classification problem. The extensive simulation results demonstrate that the proposed framework is extremely fast and precise in decision-making for offloading computation tasks compared to the conventional Lyapunov optimization-based algorithm results by ensuring the guaranteed quality of experience.

Information Technology (IT) has revolutionized the way we manage our money. The adoption of innovative technologies in banking scenarios allows to access old and new financial services but in a faster and more secure, comfortable, rewarding and engaging way. The number, the performances and the seamless integration of these innovations is a driver for banks to retain their customers and avoid costly change of hearts. The literature is rich in works reporting on the use of technology with direct or indirect impact on the experience of banking customers. Some mapping studies about the adoption of technologies in the field exist, but they are specific to particular technologies (e.g., only Artificial Intelligence), or vice versa too generic (e.g., reviewing the adoption of technologies to support any kind of banking process). So a specific research effort on the crossed domain of technology and Customer Experience (CX) is missing. This paper aims to overcome the following gaps: the lack of a comprehensive map of the research made in the field in the past decade; a discussion on the current research trends of top publications and journals is missing; the next research challenges are yet to be identified. To face these limitations, we designed and submitted 7 different queries to pull papers out of 4 popular scientific databases. From an initial set of 6,756 results, we identified a set of 89 primary studies that we thoroughly analyzed. A selection of the top 20% works allowed us to seek the most performant technologies as well as other promising ones that have not been experimented yet in the field. Main results prove that the combined study of technology and CX in the banking sector is not approached systematically and thus the development of a new specific research line is needed.

Hajj and Umrah are Muslims’ religious pilgrimages to Makkah, Saudi Arabia. Hajj is considered the largest annual gathering in the world. In Islam, every adult is obligated to conduct Hajj and Umrah at least once if he/she is in good health and can afford to do so. Hajj takes place between the 8 $^{\mathrm{ th}}$ and the 13 $^{\mathrm{ th}}$ day of Dhul Hijjah, which is the last month of the Islamic year while Umrah, the minor pilgrimage, could be done any time of the year. In Hajj, pilgrims, also known as Hajjis, travel around 15 km and pilgrims have the choice to do it by foot, bus or by train. The Hajj journey starts from Mina, which is considered the largest tent city in the world because it hosts more than 10,000 fire resistant tents, and includes various religious rituals. Around 3 million Muslims from more than 183 countries make Hajj every year and the Kingdom of Saudi Arabia is working on increasing this number. Since 1989, researchers in various ICT domains have tried leveraging their expertise in solving challenges and issues related to Hajj and Umrah. Although several papers have surveyed efforts using technology in Hajj and Umrah research, none of them was comprehensive. In this paper, I classified research efforts that used information and communication technologies for solving Hajj and Umrah challenges over the past 33 years based on their applications in 10 categories. I also identified more than 30 technologies used by researchers all over the world to address Hajj and Umrah issues and group research efforts based on these technologies for ease of access. Furthermore, open challenges were discussed and new technologies that could be used to address these challenges were proposed.

Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real-life applications such as measuring air pollution levels, road traffic information, etc. In literature, this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers has multiple homogeneous sensing tasks. Each task requester reports a bid and the number of homogeneous sensing tasks to the platform. On the other side, multiple IoT devices report the ask (the charge for imparting its services) and the number of sensing tasks they can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. Given the above-discussed scenario, the objectives are: (1) to determine a set of quality IoT devices for each of the tasks held by the task requesters, and (2) to select a subset of quality IoT devices from among the available quality IoT devices for each of the sensing tasks. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters that also keep into account the quality of IoT devices. Through theoretical analysis, it is shown that the mechanism is truthful, budget balanced, individually rational, computationally efficient, correct, and prior-free. Further, probabilistic analysis is carried out to estimate the average number of tasks that get executed for any task requester. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms on the ground of truthfulness, budget balance, satisfaction level, average incentive, and computational efficiency.

Smart City has been an emerging research domain for Government, Businesses, and researchers in the last few years. The Indian government is also interested and investing lots of funds to develop smart cities. These cities are technology-based and require interdisciplinary research and development for successful implementation. Over the last few decades, various technological interventions have created a tendency to provide smart everyday objects to make human life more comfortable. The emergence of the smart city paradigm is a response to creating a future city that guarantees the well-being and rights of its citizens from the perspective of industrial development: industry, urban planning, environment, and sustainable development. There are several subdomains in the smart city for the research. To work with the different subdomains in a smart city, proper guidance about the background of the smart city is required. This research paper is a guide for the same. This research paper represents a systematic literature review of the smart city domain. This paper carries out a systematic review of research papers published in various well-reputed journals like IEEE, Springer, Elsevier, etc., between 2011 and 2021. This paper will help the government, businesses, and researchers aiming to enhance the smart city concept. Initially, this paper discusses the origin and emergence of this concept, followed by a few definitions and characteristics with the real roadmap and primary supporting pillars of the smart city. This paper discusses a typical architecture having different layers like Sensing, Transportation, Data Management, and Application Layers. There are various supporting technologies and platforms for the smart city; hence implementations are impossible without these technologies and media. This research paper discusses different components of the smart city. A broad literature survey is being done to observe various challenges, opportunities, and future trends in the smart city. This research paper can guide the researchers and provide the research direction in the smart city domain.

Physics-based digital twins for heavy equipment provide a powerful tool for improving operation and maintenance activities. In contrast to data-driven models, they present more explainable and confident results but require more computational power. Besides the problem of physics-based digital twins creation, there is a task of managing their lifecycle, including their execution, maintenance, storage, and updating. The features distinguishing this kind of digital twins are the mobility of the real counterpart, operation in remote locations, long lifecycle, information sensitivity, and gaps in information technology awareness among the equipment owners and users. This paper presents a methodology and reference architecture for a set of interconnected systems capable of running digital twins of heavy equipment in such conditions. A data model for preserving digital twin-related information for decades of machine operation is described. Operating-system-level virtualization technologies are used to run digital twins in a heterogeneous execution environment. An example of the reference architecture implementation is presented for the physics-based digital twins of a mobile log crane. The experimental part of the paper includes a comparison of computing time for different types of digital twins in different execution environments. It highlights the peculiarities related to running physics-based digital twins in containers. Experiments were performed using the Amazon cloud platform, an edge computing system represented by a single-board microcomputer based on ARM architecture, and a virtual machine on a desktop personal computer. Experimental results show that physics-based digital twins for the analysis of the multi-body dynamics can be run within the proposed architecture with real-time performance in all three types of execution environments. The paper demonstrates the practical implementation of physics-based digital twins for heavy equipment and defines directions for future research in this field.

Machine learning has been applied in healthcare domain for the development of smart devices to improve the life of the elderly persons in the society. Taking care of elderly person in the society is a critical issue that need automation. To proffer solution, many researchers developed deep learning algorithms smart devices for detecting elderly behavior to improve the elderly healthcare. Despite the progress made in the applications of deep learning algorithms in elderly healthcare systems, to the best of the author’s knowledge no comprehensive recent development has been published on this interesting research area especially focusing on deep learning. In this paper, we presented a comprehensive recent development on the advances, methods and real world applications on developing smart devices for detecting elderly behavior for use in smart home, smart clinic, smart hospital and smart elderly nursing home for elderly person’s healthcare. Theories of the deep learning algorithms, recent development recorded as regard to the applicability of deep learning in elderly healthcare systems and case studies were discussed. A taxonomy based on the data extracted from the applicability of deep learning algorithms in elderly healthcare systems is created to ease pointing out areas that need more attention. The article shows that the deep learning algorithm that received tremendous attention from researchers is convolutional neural network architecture and its variants. To help in future development of the research area, we highlighted the challenges associated to the applicability of deep learning algorithms in elderly healthcare system and pointed out new point of view for future research. The research community can use our review as a benchmark for proposing novel deep learning algorithms based smart devices to detect elderly behavior for elderly healthcare systems. Industries and organizations can use the paper as a guide in selecting machine learning based smart device for detecting elderly behavior for elderly healthcare support.

A middleware software can be seem as an abstraction layer between hardware and user applications, that facilitates the development and deployment of services in various scenarios, such as those found in Home Energy Management Systems (HEMS). There are several middleware proposals for HEMS, with most of them taking the cloud computing approach. This approach is unconcerned about computing resources but raises a dependency on external connections. This paper presents a middleware for energy management systems, based on the concept of edge computing for smart homes. The paper presents a reference model for the proposed architecture, considering specific requirements for this type of application. The proposed architecture employs the concept of microservices for data access and system configuration. The proposed middleware is designed to work with embedded systems under computational constraints, such as processing capability and storage, to reduce costs and allow its application closer to the user. The middleware is open and customizable to meet the developer’s needs. The proposed solution was implemented and tested in a university laboratory, as well as at the Eldorado Research Institute to confirm the effectiveness of the middleware. The proposal stands out from others found in the literature as it can be implemented using low cost hardware. In addition to using microservices concepts, the proposed middleware is a valuable option for applications that need an edge computing approach. A performance analysis was carried out, using low cost hardware with limited resources. The results show that the proposal can handle a significant number of devices, offering low latency and low error rate, and consuming few processing resources and memory.

Forecasting the output power of renewable energy sources is of an utmost importance for maintaining the stability of the electricity grid and ensuring uninterrupted service. Internet-of-Things (IoT) devices equipped with sky imagers are often used to estimate solar irradiance whose value is essential for forecasting solar energy. This paper considers distributed sky imagers whose data are transmitted through the Internet to a cloud for possible storage, forecasting, and/or performing other signal processing operations. The data comprise a collection of images, and this makes their streaming to the cloud a burden, i.e., they require a large bandwidth. In an attempt to deal with that, an IoT system is proposed in this paper such that it collects, compresses, transmits, and reconstructs those images with as little bandwidth as possible. More specifically, an IoT device multiplexes the collected images and compresses them using a deep learning algorithm, the encoder of a convolutional autoencoder (CAE) in particular. It, then, transmits the compressed images to a cloud server where they could be decompressed with high-fidelity using the decoder of the CAE. As the core to the proposed system, the performance of the CAE algorithm is evaluated using a publicly available dataset of sky images. Results indicate that the images can be compressed to 2% of their original size (i.e., compression ratio of 98%) and reconstructed again with high fidelity, i.e., average structural similarity index measure (SSIM) of 99%. Hardware implementation of the CAE algorithm has also been attempted using a Raspberry Pi platform representing the IoT device with an average processing speed of 6.6681 seconds per frame. This speed well meets practical requirements given the low frame-rate of sky imagers.

Software code is present on multiple levels within current Internet of Things (IoT) systems. The quality of this code impacts system reliability, safety, maintainability, and other quality aspects. In this paper, we provide a comprehensive overview of code quality-related metrics, specifically revised for the context of IoT systems. These metrics are divided into main code quality categories: Size, redundancy, complexity, coupling, unit test coverage and effectiveness, cohesion, code readability, security, and code heterogeneity. The metrics are then linked to selected general quality characteristics from the ISO/IEC 25010:2011 standard by their possible impact on the quality and reliability of an IoT system, the principal layer of the system, the code levels and the main phases of the project to which they are relevant. This analysis is followed by a discussion of code smells and their relation to the presented metrics. The overview presented in the paper is the result of a thorough analysis and discussion of the author’s team with the involvement of external subject-matter experts in which a defined decision algorithm was followed. The primary result of the paper is an overview of the metrics accompanied by applicability notes related to the quality characteristics, the system layer, the level of the code, and the phase of the IoT project.

5G wireless communication systems provide massive system capacity with high data rates, very short low-latency, and ultra-high reliability, in addition to high connection density with a positive experience on smart cities and the Internet of Things (IoT). Future networks are anticipated to revolutionize typical applications such as the enhanced mobile broadband services (EMBB), ultra-reliable low latency communication (uRLLC), and massive machine-type communications (mMTC) anywhere and everywhere. This rationalizes the need to investigate the sustainable elements of 5G networks in smart cities to understand how 5G networks can be more environmentally- friendly and energy-efficient. This paper aims to investigate how 5G networks can act as key enablers in achieving sustainability in smart cities, using a macroscopic review. An overview of 5G communication networks and several 5G technologies used in smart city applications to enhance sustainability is presented. This is followed by investigating the indicators that measure sustainability in 5G networks across the environmental, social, and economic dimensions; and sub-dimensions such as energy efficiency, power consumption, carbon footprint, pollution, cost, health, safety, and security. The results show that the majority of research papers focus on the environmental dimensions of sustainability (42%) when attempting to address sustainability in 5G systems and smart cities. The findings also showed a huge interest in the economic (37%) and social (21%) dimensions as well. Further, when examining the sub-dimensions, it was found that most of the studies focused on energy efficiency (20%), power consumption (17%), and cost (15%).

This paper proposes the classification standard of supply chain structure from three different aspects: the complexity of the supply chain node enterprises, the reliability of the supply chain, and the optimization goal. In addition, this article starts from the characteristics of agriculture itself, according to the actual needs of the agricultural supply chain, considers the feasibility of the solution strategy from different sides, and gives related models, which solve the related problems in the agricultural supply chain to a certain extent. At present, there are many resource allocation algorithms at the edge of mobile networks, but the existing resource allocation algorithms still have the problem of high computational complexity and can still solve the above problems in resource utilization optimization. In order to solve these problems, this paper proposes a system architecture of the agricultural industry Internet of Things based on edge computing. The main motivations of this article are to improve the stability and usability of the system, and to enhance agricultural wisdom, so as to provide a reference for the development of precision agriculture. According to this method, Firstly, from the perspective of how to improve the energy efficiency of mobile terminals, methods to improve the energy efficiency of mobile terminals are studied in multi-user systems where mobile terminals compete for MEC servers with limited computing resources. Then, this paper focuses on the waste of computing resources caused by user movement under the scenario of limited server coverage in MEC system and studies the problem from the perspective of effective management of computing resources. Experimental results show that the proposed algorithm has good performance.

Manufacturers of smart home consumer devices like home theatres, music players, voice-based assistants, smart lighting, and security cameras have widely adopted the Internet of Things (IoT). These devices pose a significant security risk to consumers because the devices are exposed to mobile applications and cloud-based services with known security vulnerabilities. Most current home consumer devices provide little or no information about the level of security they afford. Since most consumers are not tech-savvy, it is currently difficult for a consumer to make an informed decision about which consumer device model (e.g., smart television model) has the best security. Hence, consumers need an objective security ranking of each type (e.g., security cameras) of home consumer devices. This paper proposes a novel methodology to systematically build such security rankings for home consumer devices. The proposed methodology can be applied by utilizing data from any security assessment study. The paper discusses previous efforts in applying Analytic Hierarchy Process (AHP) to rank security risks in general. The paper also presents a systematic survey of security vulnerabilities of smart home consumer devices when viewed from an IoT lens. Using the proposed methodology, a case study, employing an AHP model for ranking commonly used home consumer devices including home theatres, security cameras, smart lighting, smart speakers, video surveillance, smart switches, home automation systems, home security systems, smart routers, wireless doorbell cameras, and home audio systems, was developed. Relative security rankings for each type of consumer device were derived from the AHP model. According to the AHP model, network security was the primary driver of smart home device security with a priority of 0.6893 while application security had the least priority of 0.0591. Critical Vulnerabilities were the most important for device security (priority=0.4397), Man-in-The-Middle attacks for network security (priority=0.2019), exploitable services for cloud security (priority=0.26), and sensitive data for application security (0.7626). The AHP model was internally consistent (Consistency Ratio < 0.1). Sensitivity analysis showed that the AHP model was robust against pairing assumptions.

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN concerning Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, from 2010 to 2021. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for for research community in identifying the most adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper by stating the open research issues and new directions for future research.

Most countries in the world are currently faced with a series of public procurement challenges. Moreover, the large volumes of public procurement and the impact it may have of the global economy, the environment and the society at large justify a research study aimed at achieving sustainable and smart procurement. Smart public procurement intensively relies on emerging technologies and it is both an international priority and a challenge to achieve it. This paper is aimed at addressing such procurement-specific challenges. This study reflects the current status and the trends in public procurement, as well as the manner in which Blockchain and the Internet of Things (BIoT) may lead to a beneficial change in the field. In order to analyse the impact of BIoT, we are putting forward an assessment model comprising the definition and the description of six hypotheses. They are validated both by reference to the current knowledge status and via the analysis of the data collected in a survey which was conducted in Romania. It was aimed at collecting and analysing the data from the main stakeholders as well as at formulating recommendations/actions related to the modernisation of the current system. The study uses structural equation modelling (SEM) to validate the proposed model and to establish the relationships between the adoption of BIoT and smart, sustainable and transparent public procurement. At the same time, we analyse the links between the adoption of BIoT and aspects such as corruption and fraud, the challenges related to technological integration and the need to reengineer organisations, as well as national and international policies. Following our analyses, there emerged that BIoT adoption has a positive impact on the achievement of sustainable public procurement processes (the highest effect), on transparency and the trust in public procurement, on reducing corruption and fraud in public procurement and on the achievement of smart public procurement. The paper provides theoretical and practical contributions that should support solutions to the current major challenges and represent a vehicle for innovation and sustainable development alike.

The emergence of the Internet of Things (IoT) has enabled billions of devices that collect large amounts of data to be connected. Therefore, IoT security has fundamental requirements. One critical aspect of IoT security is data integrity. Cryptographic hash functions are cryptographic primitives that provide data integrity services. However, due to the limitations of IoT devices, existing cryptographic hash functions are not suitable for all IoT environments. As a result, researchers have proposed various lightweight cryptographic hash function algorithms. In this paper, we discuss advanced lightweight cryptographic hash functions for highly constrained devices, categorize design trends, analyze cryptographic aspects and cryptanalytic attacks, and present a comparative analysis of different hardware and software implementations. In the final section of this paper, we highlight present research challenges and suggest future research topics related to the design of lightweight cryptographic hash functions.

Indoor localization techniques have been extensively studied in the last decade. The well-established technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization.

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. Offloading tasks to the fog that is closer to IoT users for processing has become a means to ensure that tasks are completed quickly. Fog computing cannot only reduce the congestion of the backbone network but also ensure that the task is completed within the specified time. Since fog resources are limited, there will be resource competition among IoT devices. How to quickly and efficiently make an optimal computation offloading decision for individual selfish IoT devices is a fundamental research issue. This article regards the process of multiple IoT devices competing for fog devices as a game and proposes a distributed computation offloading algorithm. The goal is to optimize the balance of computation delay, energy consumption, and cost for fog nodes. The competition between IoT nodes eventually reaches an equilibrium point, that is the Nash equilibrium point. We prove the existence of Nash equilibrium by Weighted Potential Game. In addition, if a large number of IoT devices select the same node for offloading, which will cause the fog node to run out of power and make some networks unable to work normally. Further, causing part of the network to be paralyzed. Therefore, the paper considers the fairness of offloading to extend the network life cycle. A calculation rate adjustment algorithm is designed for the fairness of offloading to ensure that fog nodes do not run out of power and fail. This paper not only fully considers the performance of the IoT device, but also considers the fairness of the fog. Numerous experiments proved the effectiveness of the proposed algorithm.

The ever-growing smart applications will expand the Internet of Things (IoT) ecosystem to connect over 75 billion devices by 2025. IoT ecosystems comprise sensors that act as data suppliers and applications where monetary transactions are required to compensate the data producers. This signifies the importance of IoT payments and marketplaces to facilitate micro-transactions of billions of connected devices in the IoT ecosystem, which is heterogeneous, decentralized, and diverse. However, realizing such an ecosystem raises multiple challenges such as overcoming poor inter-operability, resource constraints, and security and privacy vulnerabilities of IoT devices and platforms. Blockchain is a Distributed Ledger Technology (DLT) that can be identified as a potential solution to overcome the challenges in realizing IoT payments and marketplaces. This is due to the characteristics of blockchain such as decentralization, traceability, immutability, and non-repudiation. This paper presents a comprehensive survey on blockchain-based IoT payments and marketplaces. This paper provides a brief introduction to the concepts of IoT payments and IoT marketplaces. Then the technical challenges involved in realizing IoT payment and marketplaces are discussed by highlighting the blockchain-based solutions. Furthermore, blockchain-based smart applications which use IoT marketplace and IoT payment concepts are presented marking the role of blockchain in each of the application. Subsequently, the paper discuss the integration challenges while also highlighting possible solutions. It is envisaged that this paper would shed light on the development of blockchain-based solutions to realize IoT payments and marketplaces.

Wireless Sensor Networks (WSNs) represent a key component in emerging distributed computing paradigms such as IoT, Ambient Intelligence, and Smart Cities. In these contexts, the difficulty of testing, verifying, and monitoring applications in their intended scenarios ranges from challenging to impractical. Current simulators can only be used to investigate correctness at source code level and with limited accuracy. This paper proposes a system and a methodology to model and verify symbolic distributed applications running on WSNs. The approach allows to complement the distributed application code at a high level of abstraction in order to test and reprogram it, directly, on deployed network devices. The proposed intelligent architecture enables the execution of distributed applications and the verification of the supplied correctness conditions. This paper shows the feasibility of the proposed approach and its effectiveness even when networks include resource-constrained nodes with some sample applications and quantitative experiments measuring the overhead introduced by the monitoring operations.

With the continuous improvement of modern scientific construction, urban development is moving in the direction of digitization. Under the background of digital technology of Internet of things, many cities have new challenges and opportunities in the construction of a new generation. Urban furniture design plays an important role in public areas and activity spaces. It can bring many interactive and spatial elements to people’s daily life. At the same time, the functionality and reliability of urban furniture design are also the main contents we pay attention to. How to reflect the applicability of design and evaluate the performance of products is the research content of this paper under the background of Internet of things and digitization. Firstly, it analyzes the current situation of urban furniture design and the specific needs in the digital background. This paper explores the impact of the above factors on the safety of urban furniture from the aspects of urban development, economic situation and user groups. Using Internet of things data modeling and estimation methods to analyze the data changes contained in urban furniture design. Judge the performance changes of furniture design in actual use according to the data information, and analyze the feasibility of people in use. Finally, in the big data environment, with the support of Internet of things technology, this paper studies the sharing function in urban furniture design, and evaluates the feedback effect of shared urban furniture. The results show that urban furniture design under the digital background of Internet of things reflects good feasibility and applicability in modern urban construction. Digital urban furniture has gradually become a new symbol of a city. This paper uses data mining to obtain and analyze the data in the process of urban furniture design, and realizes the standardized processing of user behavior data. It not only meets people’s daily needs and the needs of intelligent equipment, but also strengthens the importance of urban furniture design and use in modern urban construction. Compared with the traditional Internet, the Internet of things can combine the wireless transmission network with the actual objects to achieve the purpose of accurately transmitting information.

Due to the large variety of Internet of Things (IoT) platforms, selecting the right one to implement an IoT solution is a tough task. To mitigate right selection by the developer, this paper presents a Systematic Multivocal Mapping Study on IoT platforms and its main software elements, to define their anatomy considering how they were studied by the market analysts and academia. By using a precise protocol defined on this work, it was possible to select 50 academic articles and industry reports that perform IoT platform descriptions, evaluations and comparisons. As results, this paper identified the most important IoT platforms are AWS IoT, Azure IoT, Watson IoT, PTC ThingWorx and Google IoT. Its main capabilities are Interoperability, Security & Privacy, Developer Support, Data Management, Device Management and Services Management. It was also defined an architectural model with the main platform components highlighted according to their relevance, the main communication models (Publish/Subscribe and REST APIs) and the common API that should be implemented by the IoT platforms.

The Internet of Things is based on a communication network to automatically receive various information from the natural world. It uses intelligent objects with perception, communication and computers to automatically receive various information from nature. All independently managed physical objects are connected to each other to achieve integrated perception, reliable transmission and intelligent processing, and to establish intelligent information between people and objects, and objects and object service systems. This article mainly introduces the impact of industrial agglomeration on the regional economy in a simulated intelligent environment based on machine learning. This paper proposes a method to detect industrial agglomeration index to analyze industrial agglomeration. Through the establishment of the industrial agglomeration index system, the level of integration of the manufacturing industry in our city was objectively analyzed, and the impact of the integration of manufacturing industry on our city was tested empirically. Finally, the relationship between industrial integration and regional economic development was tested. The experimental results in this paper show that industrial integration in a smart environment based on simulation learning has a significant positive impact on regional economic development. Among them, the level of cooperation between the manufacturing service industry and the manufacturing industry increased by 1%, and the level of regional economic development increased by 0.025%.

As we move into a new decade, the global world of Intelligent Infrastructure (II) services integrated into the Internet of Things (IoT) are at the forefront of technological advancements. With billions of connected devices spanning continents through interconnected networks, security and privacy protection techniques for the emerging II services become a paramount concern. In this paper, an up-to-date privacy method mapping and relevant use cases are surveyed for II services. Particularly, we emphasize on post-quantum cryptography techniques that may (or must when quantum computers become a reality) be used in the future through concrete products, pilots, and projects. The topics presented in this paper are of utmost importance as (1) several recent regulations such as Europe's General Data Protection Regulation (GDPR) have given privacy a significant place in digital society, and (2) the increase of IoT/II applications and digital services with growing data collection capabilities are introducing new threats and risks on citizens' privacy. This in-depth survey begins with an overview of security and privacy threats in IoT/IIs. Next, we summarize some selected Privacy-Enhancing Technologies (PETs) suitable for privacy-concerned II services, and then map recent PET schemes based on post-quantum cryptographic primitives which are capable of withstanding quantum computing attacks. This paper also overviews how PETs can be deployed in practical use cases in the scope of IoT/IIs, and maps some current projects, pilots, and products that deal with PETs. A practical case study on the Internet of Vehicles (IoV) is presented to demonstrate how PETs can be applied in reality. Finally, we discuss the main challenges with respect to current PETs and highlight some future directions for developing their post-quantum counterparts.

The developments in information and communications technologies (ICT) come with changes in all the fields of life, including the education system. At the same time, the IoT (Internet of Things) is turning ever more important as regards the overall benefits that it brings to smart cities as well as the education system. Starting March 2020, the COVID-19 pandemic has caused fast-paced changes in education and forced the ICT integration in higher education. Despite that, the IoT is still at the beginning in the education system and the impact of its adoption is far from being fully understood. This article is aimed at describing a smart education environment and the extent to which the IOT is conducive to this desideratum. The paper also identifies and describes the most important benefits and challenges related to the adoption of the IoT in higher education. In order to analyze the impact of the IoT adoption in the education environment, the authors propose an assessment model based on six hypotheses, including their definitions and descriptions. They are validated against the Romanian higher education system, as well as a set of survey data. Structural equation modelling (SEM) is used in the study to validate the suggested model as well as to determine how the adoption of the IoT relates to intra- and extra-university connectivity, to attracting additional resources, to the teaching and learning activities, to data security and integrity as well as to education policies. The last part of the paper dwells on the analysis of a set of survey data and of the hypotheses shaped herein. The paper also includes recommendations, as well as the main conclusions, limitations and future research directions. It contributes both from the theoretical and practical perspective to the development of smart universities in the future.

A detection model of Internet of Things encrypted traffic based on edge intelligence is proposed in the paper, which can reduce the communication times of distributed Internet of Things gateways in the process of edge intelligence as well as the encrypted traffic detection model establishment time, in order to solve the problems that it is difficult to carry out efficient classification and accurate identification of the encrypted traffic of Internet of Things. In this paper, four new classification and identification methods for encrypted traffic are put forward, namely time-sequence behavior analysis, dynamic behavior analysis, key behavior analysis and two-round filtering analysis. The experimental results show that when the sample size is 1600, the encrypted traffic detection model establishment time is less than 100 seconds, and the accuracy of all the four new traffic classification methods is more than 92% and the recall rates of them are more than 83%.