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Electronic paper (e‐paper) is a reflective display technology with unique advantages, such as bistability, low‐power consumption, and high ambient contrast ratio. These features make e‐paper a promising candidate for future Internet of Things applications. Among different technologies of e‐paper, electrophoretic display (EPD) is the most successful one for commercialization. However, the edge ghosting (also known as the fringing effect) still limits the performance of EPD. Herein, we established a model of particle dynamics of electrophoresis, simulated the edge ghosting of microcapsule EPD, analyzed the edge ghosting effect, and revealed the relationship between thicknesses, dielectric constants of the back binder layer, and the edge ghosting. Two EPD panels with different thicknesses of back binder layer were demonstrated, which verifies the accuracy of this simulation model. With the proposed model, many device mechanisms and product issues can be analyzed and illustrated, which is supposed to guide the researchers in optimizing the device structure design of EPD. [ABSTRACT FROM AUTHOR]

The papers address the following key areas: ' B I Applications of IoT and digital twin in electrical power systems: A comprehensive survey i b ': This research paper reviews the applications of the Internet of Things (IoT) and digital twin technology in electrical power systems. With the growth of Internet of Things (IoT) techniques, applications have become smarter, and linked gadgets allow them to be used in many parts of power systems. ' B I A new low-cost and low-power industrial Internet of Things infrastructure for effective integration of distributed and isolated systems with smart grids i b ': This research paper provides an Internet of Things (IoT) infrastructure solution based on a newly designed low-cost microcontroller-based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. [Extracted from the article]

We develope new application, in conjunction with the Internet of things hardware and software technology, E‐Paper will be used in the use of official ID Card to research, in particular on how to use low‐resolution display of human head color processing algorithms, on NFC passive driver EPD display and on‐line storage and operation data transmission. [ABSTRACT FROM AUTHOR]

Learning-driven ubiquitous mobile edge computing: Network management challenges for future generation Internet of Things Keywords: artificial intelligence; Internet of Things; machine learning; mobile edge computing; network Management EN artificial intelligence Internet of Things machine learning mobile edge computing network Management 1 4 4 09/13/23 20230901 NES 230901 Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. Fifth, I Jafar et al. i proposes "A blockchain-enabled security management framework for mobile edge computing", which guarantees secure data storage and includes blockchain features like immutability and traceability. [Extracted from the article]

Summary: This paper provides some basic definitions associated with digital transformation in organizations and applies those definitions to accounting, electronic commerce, and supply chains. I also drill down on the dimensions associated with digital transformation, including digital everywhere, integration (across applications and with customers and partners), and the need to reengineer processes. I examine several examples of processes ranging from digitization to digital transformation. I also examine the role of people in digitally transformed organizations and some technologies that are important to continued evolution of digitally transformed organizations. Further, we explore a number of scenarios of digital transformation. Finally, these investigations result in the determination of a number of emerging research issues. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Things era, the connection between electronic devices and humans is getting closer and closer. New‐concept electronic devices including e‐skins, nanogenerators, brain–machine interfaces, and implantable medical devices, can work on or inside human bodies, calling for wearing comfort, super flexibility, biodegradability, and stability under complex deformations. However, conventional electronics based on metal and plastic substrates cannot effectively meet these new application requirements. Therefore, a series of advanced electronic devices based on flexible porous substrates (e.g., paper, fabric, electrospun nanofibers, wood, and elastic polymer sponge) is being developed to address these challenges by virtue of their superior biocompatibility, breathability, deformability, and robustness. The porous structure of these substrates can not only improve device performance but also enable new functions, but due to their wide variety, choosing the right porous substrate is crucial for preparing high‐performance electronics for specific applications. Herein, the properties of different flexible porous substrates are summarized and their basic principles of design, manufacture, and use are highlighted. Subsequently, various functionalization methods of these porous substrates are briefly introduced and compared. Then, the latest advances in flexible porous substrate‐based electronics are demonstrated. Finally, the remaining challenges and future directions are discussed. [ABSTRACT FROM AUTHOR]

In recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%. [ABSTRACT FROM AUTHOR]

Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) related and IoT enabled education and research are becoming more prevalent in today's academic environment. One of the challenges faced by educators and researchers is the availability of resources to support lab based, hands on learning and research projects. Existing resources are often highly customized, balkanized, and difficult to manage. For new technology areas such as IoT, these environments rarely provide opportunities for resource sharing and interdisciplinary collaboration. In this paper, we describe the development of a multi‐tenant, IoT centric platform, the Environmental Sensing Data Network (ESDN) meant to support plug and play sensor deployment and the tools necessary for data management. The platform turns the University campus into an open platform that can be used to easily set up IoT based labs for education, research, campus infrastructure management and community applications innovation. The paper covers the development of a sensor augmentation module facilitating easy deployment and management. The operational tools and services integration are also described. We believe the platform is an important step in facilitating distance and in person education and research. ESDN is rapidly expanding beyond the multi‐site University campus supporting a growing number of research and community projects. [ABSTRACT FROM AUTHOR]

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a system of physical objects embedded with various sensors to receive information, software, chips, and other technologies that allow connecting and transferring data to other devices through the Internet without human intervention. As the number of smart devices increase, IoT has started to be applied in many more fields. Therefore, there is a lot of information that should be processed. To manage this amount of data, some researchers proposed the usage of big data techniques. Big data are a collection of structured and unstructured data incoming with a high speed and large amounts. This paper investigates big data applications in IoT to comprehend the different published approaches using the systematic literature review (SLR) technique. This paper systematically studies the latest research methods on big data in IoT approaches published between 2016 and August 2021. A methodical taxonomy is shown for big data in IoT‐related fields consistent with the content of existing articles chosen with the SLR process in this research like healthcare, smart city, algorithms, industry, and general aspects in those environments. The advantages and drawbacks of each paper are presented, with specific proposals for stating their pros and cons open issues and advising possible research challenges in big data implementation in the IoT. The evaluation factors of big data applications in IoT are distributed as follows: security (18%), throughput (17%), cost (17%), energy consumption (15%), reliability (15%), response time (9%), and availability (9%). [ABSTRACT FROM AUTHOR]

The widespread use of networked, intelligent, and adaptable devices in various domains, such as smart cities and home automation, climate control, manufacturing and logistics, healthcare, education, and agriculture, has been hastened by recent developments in hardware and software technologies. In all these application domains, the concept of the Internet of Things helps to achieve process automation and decrease labor costs. While IoT has been an established domain for quite a while, it has seen a lot of advances and challenges in different subdomains over the years. One such subdomain is IoT Forensics which involves digital forensics concerning IoT devices, networks, or clouds. In this process of obtaining substantial evidence from the devices, networks, or cloud, a large amount of data and operations on said data are involved. Hence, looking through IoT Forensics through the methodology dealing with data, known as data analytics, is essential. This paper presents an interpretation of IoT Forensics from the standpoint of data analytics. To explain the same in detail, the paper focuses on IoT Forensics, its methodologies, and how they relate to data analytics stages. Toward the end, the paper discusses current developments in IoT Forensics from the data analytics perspective, limitations observed in the existing technologies, adoption challenges, and possible future advancements. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a crucial technology in the healthcare industry thanks to recent developments in automated data collection. Current statistics of the healthcare sector show that it is expanding at a remarkable rate. However, the performance of the healthcare system is impacted by many challenges, such as security, privacy, latency, scalability, and heterogeneity that come with advancement. Fog computing and software‐defined networks (SDN) are critical enabling technologies that help to improve IoT‐based healthcare systems by ensuring timely and reliable data provisioning for use in healthcare applications. SDN solves interoperability, device management, and network management issues and automates patient health monitoring without human interaction. SDN and fog computing in IoT‐based healthcare can optimize device communication and computing power at a low cost. SDN‐enabled fog computing has lower latency and better security than cloud‐based IoT healthcare architecture. The researchers have not entirely discovered SDN‐enabled fog computing to the best of our knowledge. This paper covers healthcare IoT technicalities and related technologies. We examined ways to merge SDN, fog computing, and IoT to improve healthcare solutions. Finally, we concluded the paper with research issues and future healthcare system improvements. [ABSTRACT FROM AUTHOR]

This paper investigates the unmanned aerial vehicle (UAV)‐assisted wireless communication network that collects the data information of Internet of things (IoT) devices deployed in the region, where the cellular networks cannot cover. Due to the numerous variety and number of IoT devices, a large amount of data generated by IoT networks needs to be collected by UAV. The goal of this paper is to minimize the UAV's cruise time with the joint optimization of IoT devices communication scheduling, UAV trajectory, and transmit bandwidth allocation. To facilitate data collection by UAVs, the data‐distance‐k‐means (d2‐k‐means) algorithm is proposed to divide IoT devices into multiple initial clusters. However, the formulated problem is mixed‐integer joint non‐convex, so it is difficult to solve directly. Since it may be with relatively high computational complexity, as an alternative, a block coordinate descent (BCD)‐based method is designed. To tackle the non‐convex problem, a successive convex approximation (SCA)‐based algorithm is also proposed. Numerical results demonstrate that the proposed scheme is able to achieve significant performance over other schemes for scenarios of UAV‐assisted wireless IoT networks to collect massive amount of data. [ABSTRACT FROM AUTHOR]

Light is a popular choice as an indoor energy source for Internet of Things (IoT) end devices. However, indoor light sources are intermittent, which can disrupt the operation of IoT end devices, potentially leading to safety concerns or inaccurate data. Therefore, there is a growing need to develop a long‐duration power supply for IoT end devices. The energy from ambient light is harnessed to charge a supercapacitor through an energy manager chip. This supercapacitor serves as the power source for IoT nodes when the ambient light is unavailable. Nevertheless, as the voltage of the supercapacitor drops below the operating threshold, the IoT end node will eventually shut down. This paper proposes a circuit that utilizes a Joule Thief circuit, booster converter, and capacitor stack‐up circuit to extract the remaining energy from the supercapacitor and boost the voltage, thereby extending the operational lifespan of IoT end nodes. Additionally, capacitor stack‐up circuits significantly enhance charging efficiency. PSpice design and simulations confirm circuit feasibility. High‐efficiency charging and long‐duration IoT nodes suggest replacing traditional batteries with supercapacitors, reducing environmental impact. [ABSTRACT FROM AUTHOR]

Parallel to the increasing use of smart speakers (aka virtual assistants), privacy concerns emerge. In this article, we provide a systematic review of the literature on privacy issues in smart speakers. Both Scopus and Web of Science databases are examined, as well as the preprint repository arXiv, to take into account the most recent contributions. That literature exhibits a continuously growing trend in the last 4 years, with the USA being the single largest contributor to the field. The topics debated in the literature concern the perception of privacy issues, their contribution to adoption choices, the identification of vulnerabilities, the proposal of possible countermeasures, and (last and least) the analysis of the legal cases that are arising. [ABSTRACT FROM AUTHOR]

ABSTRACT Shadow is a lightweight AND‐RX block cipher adapted for resource‐constrained devices. In this paper, software and hardware optimizations are proposed respectively for Shadow to enhance its implementation performance. For software optimization, this paper first proposes a data pre‐processing scheme based on the structural characteristics of the round function. It further improves the optimization effect of the barrel shifter instruction while simplifying the implementation process of the round function. Note that the optimization strategy is also applicable to other AND‐RX ciphers. Secondly, this paper proposes a new NX operation implementation scheme that can effectively reduce its instruction cycles. In round‐based architecture, experimental results show that our scheme effectively reduces code size by 24.7%, Flash consumption by 12.6%, and total instruction cycles by 25.1%. Meanwhile, in the fully unrolled architecture, our scheme reduces code size by 30.8%, Flash consumption by 29.8%, and total instruction cycles by 28.1%. For hardware optimization, this paper proposes a low‐resource implementation scheme by constructing a generic formula for NX operation. In ASIC implementation, our scheme reduces hardware resources by 72.3%. In FPGA implementation, the number of LUTs and Slices is reduced by 30% and 28.6%, respectively. Overall, the proposed optimization scheme for Shadow has better performance in hardware and software implementation. [ABSTRACT FROM AUTHOR]

Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing. [ABSTRACT FROM AUTHOR]

Flow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT. [ABSTRACT FROM AUTHOR]

Background: Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision‐making. Skin lesion segmentation from images is a crucial step toward achieving this goal—timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non‐malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. Materials and methods: This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. Results: We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. Conclusion: We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian‐Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) connects different objects in the physical world to the Internet, and various Internet protocols are used to provide communication services to a large number of these embedded devices termed as smart devices. But, these devices are resource‐constrained, low configured, and have very low power storage capacity, which depends on the services offered by the protocols. For the exchange of information to the end‐users, smart devices communicate through an open channel, such as the Internet, which is not secure enough. Moreover, efficient delivery ratio, secure data forwarding are not achieved because of the enormous amount of data produced by these smart devices and the possibility of security threats. So there is a need to devise a secure and reliable data dissemination scheme for the IoT environment. Motivated from the these facts, this paper presents a systematic review and propose a solution taxonomy for secure data dissemination techniques for various smart IoT‐based applications. This paper also includes a comparison of the state‐of‐the‐art data dissemination techniques used for the Internet of Vehicles (IoVs), Internet of Drones (IoDs), and Internet of Battlefield Things (IoBTs) applications along with their merits and demerits. Finally, the research challenges and possible countermeasures are also discussed in detail, which gives insights to the beginners who want to start work in this emerging area. [ABSTRACT FROM AUTHOR]

With the continuous maturity of big data technology and open source community in the internet of things (IoT) environment, the intelligent system architecture and ecosystem are booming and developing, and the commercial supporting tools are increasingly fast. In the paper "Information Construction of Construction Project Management Based on Internet of Things Blockchain" the authors addressed blockchain-based pattern mining method using DBSCAN algorithm for system data information analysis in the IoT. The paper entitled "Research on the construction of accounting information audit quality control system based on blockchain" proposed a blockchain-based audit quality control system for improving information security issue. [Extracted from the article]

An ultra‐low quiescent current tri‐mode DC–DC boost converter is proposed in this paper, which can efficiently handle loads from 1 μA to 300 mA, providing 5‐V output from 3 to 4.2‐V input. Hysteresis current mode control is adopted to realize seamless mode switching between continuous conduction mode (CCM) and discontinuous conduction mode (DCM). In order to deal with the light load situation, this paper proposes a quasi burst mode (QBM). In this mode, most of the modules are turned off and only the QBM controller, low‐power current bias and sample and hold circuit are retained, which significantly reduces the static current of the converter, and thus improves the light load efficiency. Meanwhile, the average current of error amplifier and comparator are reduced significantly by dynamic biasing. Finally, the efficiency of the converter is greatly improved and kept at a reasonable output ripple. The proposed converter is implemented in a 0.13‐μm BCD process design and manufacturing. The test results show that the quiescent current of the converter is only 780 nA, which can achieve a peak efficiency of 95.6%, and ensure that the efficiency is maintained above 80% in the load range of 10 μA to 300 mA. [ABSTRACT FROM AUTHOR]

Precise timing plays a key role in the time‐sensitive industrial Internet of Things (IIoT). However, precise time synchronization requires more frequent packet exchange, which consumes more communication bandwidth and energy. This is a particular challenge in battery‐powered wireless nodes, and low communication costs have become an important factor in clock synchronization. To address the challenge of achieving low communication cost clock synchronization in distributed wireless sensor networks, this paper proposes an improved event‐triggered control and synchronization scheme with a novel asynchronous broadcast packet exchange protocol. Unlike the traditional event‐triggered control scheme which is based on synchronous polling packet exchange, this proposed asynchronous broadcast packet exchange is more communication efficient and requires fewer number of packet exchanges. And it is worth noting that the proposed algorithm in this paper is a distributed algorithm and does not require real‐time acquisition of information from neighbouring nodes. Finally, a numerical example is given to illustrate the effectiveness of the proposed event‐triggered control strategy. The efficiency and precision of the proposed clock synchronization method is evaluated by intensive simulations, which show that the number of packet exchange is reduced by 60% for a moderate IIoT network and is particularly useful for large scale network. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks. [ABSTRACT FROM AUTHOR]

Summary: In order to solve the problem that IP address jump cannot be synchronized in real time due to the unstable speed of network data transmission in the Internet of Things system, in this paper, sliding window mechanism is introduced into network communication, and an Internet of Things system with network IP address jump function is constructed. The system adopts time synchronization scheme based on PTP protocol and IP address hopping strategy based on sliding window. The system includes three modules: time synchronization module based on PTP protocol to enhance security performance, address hopping module based on OpenFlow as core technology, and SDN routing module. The system can realize the security defense function of mobile devices on the Internet of Things. Finally, through the experimental test, it is found that, when the system time error is less than 18 s, the system's packet loss rate is stable and low. The anti‐attack ability is very strong. [ABSTRACT FROM AUTHOR]

The domain of cyber‐physical systems (CPS) has recently seen strong growth, for example, due to the rise of the Internet of Things (IoT) in industrial domains, commonly referred to as "Industry 4.0." However, CPS challenges like the strong hardware focus can impact modern software development practices, especially in the context of modernizing legacy systems. While microservices and DevOps have been widely studied for enterprise applications, there is insufficient coverage for the CPS domain. Our goal is therefore to analyze the peculiarities of such systems regarding challenges and practices for using and migrating towards microservices and DevOps. We conducted a rapid review based on 146 scientific papers, and subsequently validated our findings in an interview‐based case study with nine CPS professionals in different business units at Siemens AG. The combined results picture the specifics of microservices and DevOps in the CPS domain. While several differences were revealed that may require adapted methods, many challenges and practices are shared with typical enterprise applications. Our study supports CPS researchers and practitioners with a summary of challenges, practices to address them, and research opportunities. [ABSTRACT FROM AUTHOR]

The application of internet of things (IoT) devices in 5G/B5G has benefited many industries, such as automated production, smart agriculture and autopilot, and has provided access to billions of smart devices. However, the rapid growth of IoT devices has also brought network security issues. The purpose of this paper is to study the network intrusion detection model based on quantum artificial fish population and fuzzy kernel clustering algorithm. In this paper, the network intrusion detection model based on traditional fuzzy C‐means (FCM) clustering has poor classification effect and is prone to local extremum. A semi‐supervised fuzzy kernel–clustering algorithm based on quantum artificial fish group is proposed. The algorithm adopts a small amount of tag data and many unknown tag data to generate the classification of network intrusion detection and constructs a new objective function of FCM algorithm by means of kernel distance. In the simulation experiment of KDD Cup 99 network intrusion detection data, compared with the intrusion detection model based on FCM, particle swarm optimization‐FCM, the detection rate of the proposed algorithm in the known attack and unknown attack is 92.5% and 84.9%, respectively. The results show that the proposed intrusion detection algorithm has better detection rate. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real‐time data‐centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data‐centric approaches. This paper systematically categorizes the 48 recent articles on data‐driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data‐centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data‐driven approaches in IoV. [ABSTRACT FROM AUTHOR]

Summary: In today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further. [ABSTRACT FROM AUTHOR]

Summary: The widespread use of Internet of Things devices has increased the demand for lower cost and more efficient lightweight ciphers. However, there is a difficult trade‐off between cost and efficiency for lightweight block ciphers. The optimizations of area and throughput are important for some constrained environments. This paper proposes two novel hardware architectures for the LILLIPUT cipher. In the novel low area structure, a new permutation layer is provided for LILLIPUT. The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The experimental results show that the number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. The total area is reduced by about 18%. For high throughput structure, this paper provides 2‐round, 5‐round, and 15‐round loop unrolling designs for LILLIPUT to improve throughput. The experimental results show that the throughput of the 5‐round loop unrolling structure reaches a good level, which is relatively the most cost‐effective. In practical application, ciphers can be unrolled implementations according to the needs of devices to improve the execution speed, which can greatly reduce the execution time and complexity of the algorithm. [ABSTRACT FROM AUTHOR]

To address the IoT security problem, in this paper we propose and evaluate the DDoS attack mitigation method based on blockchain, and construct a DDoS abnormal information detection and sharing model. The obtained experimental results show that when the number of decision trees increases, the training time of the DDoS attack detection model based on the RF model grows with a minimum trend of 14 s. The testing time is finally maintained at 1 s, and the recognition accuracy of DDoS attacks keeps improving, ultimately reaching over 99.8%. If the amount of DDoS abnormal traffic information exceeds 100 pieces and 2000 pieces, it only takes 0.1 and 5 s to sign the DDoS abnormal traffic information using ECDSA algorithm digitally. The signature verification only takes 0.1 and 9 s, respectively. And compared to conventional network physical system IoT security architecture, a network physical system IoT security architecture that integrates AI empowerment, blockchain, and SDN integration has a higher joint defense success rate. It can be explained that this scheme will be conducive to promoting joint defense against DDoS attacks and ensuring the security of the Internet of Things. [ABSTRACT FROM AUTHOR]

This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data. [ABSTRACT FROM AUTHOR]

Climate change, land degradation, and limited land and water resources have challenged our ability to meet the food demand of a rapidly growing population. To tackle this challenge, modern agricultural systems are relying on new technologies like the Internet of Things (IoT) to improve agricultural productivity and resource use efficiency. Although IoT has gained considerable attention in the last few years, the key concepts of IoT and their applicability across different domains of agriculture are still new to many researchers, practitioners, managers, and policymakers. In this paper, we provide a comprehensive review of the use of different IoT platforms, wireless sensor networks, and other associated technologies like remote sensing, cloud computing, and big data analytics in digital agriculture. The review also explores the use of communication technologies, microcontrollers, and machine learning in smart irrigation and decision support systems. The necessity of interoperability (data transfer and communication without human interference) among devices is discussed in detail with regard to facilitating and exchanging agricultural data more effectively. The discussion also includes opportunities and challenges in standardizing IoT; developing energy‐efficient and affordable technologies; and improving data collection, transfer, storage, processing, security, anonymity, and privacy. This paper further calls for collaborative research in sensor and communication technologies in the agricultural sector. Core Ideas: Different platforms, sensors, and structures of IoT in digital agriculture were reviewed.The application of IoT in data management, interoperability, and precision farming was reviewed.Potential of IoT to improve agricultural quality and productivity was highlighted.Challenges and opportunities of IoT in digital agriculture were discussed. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) and machine learning (ML) technologies has triggered smart agricultural systems. In smart agriculture, irrigation management plays a major role to reduce water waste. The monitoring settings, hardware modules, communication technology, and storage systems used in smart irrigation systems were analyzed to determine the optimal nature of water flow. This assessment aims to give an overview of the current state of the irrigation system by taking into account weather and soil moisture. This paper provides a comprehensive review of the utilization of various hardware modules in smart irrigation systems. Moreover, various communication technologies that aid in data transfer for efficient smart irrigation are reviewed. The ML method used for prediction is also evaluated, as well as based storage technologies like the cloud and databases used to store data for predictive irrigation systems. As a result, the paper provides an overview of all the factors that contribute to irrigation systems' smart operation as well as potential future paths for improving agricultural systems through IoT. Core Ideas: Irrigation management is crucial in smart agriculture for minimizing water waste.This apper presents the ideal type of water flow that was determined by smart irrigation systems.Monitoring configurations, hardware modules, communication technology, and storage systems were analyzed in the smart irrigation system. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has become very popular as it has numerous applications in the industrial and research domain. Moreover, the features of IoT systems play a crucial role in the development of smart cities. It enables smart cities and their subsystems to monitor, control, and manage heterogeneous devices remotely by extracting and communicating real‐time data. However, automated IoT systems are vulnerable to many security threats like tempered protocols, device hijacking, and unauthorized access. Motivated by the aforementioned discussion, this paper addresses the security requirements of an essential subsystem of smart city architecture, that is, IoT‐based smart homes. Based on the features and functionalities of smart homes, the risk of security violations in the system behavior needs to be analyzed This paper explores various security threats in a smart home environment and proposes a context‐aware security‐based scheme to prevent and detect possible threats. Results show that the proposed scheme is superior compared to the traditional schemes considering parameters such as the performance, cost, and maintenance requirements. [ABSTRACT FROM AUTHOR]

Summary: In recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud‐edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud‐edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud‐edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud‐edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side‐by‐side taxonomy is proposed to categorize the relevant studies on cloud‐edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented. [ABSTRACT FROM AUTHOR]

Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

Summary: With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet‐based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain‐enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain‐enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource‐constrained MEC devices. In addition, a smart contract‐based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost. [ABSTRACT FROM AUTHOR]

Zeroing neurodynamics methodology, which dedicates to finding equilibrium points of equations, has been proven to be a powerful tool in the online solving of problems with considerable complexity. In this paper, a method for underwater acoustic sensor network (UASN) localisation is proposed based on zeroing neurodynamics methodology to preferably locate moving underwater nodes. A zeroing neurodynamics model specifically designed for UASN localisation is constructed with rigorous theoretical analyses of its effectiveness. The proposed zeroing neurodynamics model is compatible with some localisation algorithms, which can be utilised to eliminate error in non‐ideal situations, thus further improving its effectiveness. Finally, the effectiveness and compatibility of the proposed zeroing neurodynamics model are substantiated by examples and computer simulations. [ABSTRACT FROM AUTHOR]

The demand for quick, accurate, and affordable point‐of‐care (POC) devices increases with the advancement in the dimensions of nanotechnology and digital interfaces (Internet of Things). The future of diagnostic requires the platform which can provide us the following benefits i. e., on‐site detection, qualitative as well as quantitative analysis, easy to use, portable, low sample requirement, cost‐effective, and have multiplexing proficiency. Multiplex biosensing platforms (MBPs) have the above following advantages so are going to be mostly used in various healthcare applications in near future. MBPs have the potential to fulfill the 'ASSURED' criteria specified by the World Health Organization (WHO) for remote‐limited settings. This review paper focuses on miniaturized platforms that have multiplexing benefits for the bioanalysis of different clinical samples related to various healthcare applications. In addition to this, screening of pesticides, antibiotics, and hazardous metal ions with these surface‐engineered devices has also been accounted in food and environmental samples. Some of the advanced techniques including microfluidics (Lab‐on‐a‐chip), wearable smart devices, and CRISPR/Cas system for multiplexing applications are briefly described here. Furthermore, various needs, challenges, and prospects in commercializing these multiplexed surface‐engineered devices have been discussed in this review. [ABSTRACT FROM AUTHOR]

Summary: Internet of Mobile Things (IoMT) have become very popular recently. The routing protocol for low power and lossy networks (RPL) is standardized for static topologies. However, mobility is the nature of IoT. Mobility serves as a promising candidate to harness hand‐off time issues, delay in data transmission, overhead, and low packet delivery rate (PDR) effectively. This study presents a comprehensive account of the mobility‐aware RPL‐based routing protocols to validate and compare the experimental results. Remarkably, classification methods are used in many articles. The aim is to introduce significant research efforts to improve RPL objective functions (OF) performance in hand‐off time, PDR, delay, overhead, and so forth. In this regard, a complete analysis of the existing routing protocols in IoMT has been presented to compare the results. The main focus of this study is on approaches that proposed new OFs for supporting mobility in RPL. Two main categories are considered to study RPL‐based routing protocol mechanisms: The mobile and static sink. The related studies on the mobile sink are divided into three groups: Single metric‐based OF, composite metric OF, and hybrid routing protocols. Also, the related works based on the static sink are categorized into four groups: Fuzzy logic‐based OF, trickle timer‐based OF, composite metrics‐based OF, and modification control messages‐based OF approach. This paper presents a detailed comparison of mechanisms in each category. It also highlights the pros, cons, open issues, and evaluated metrics of each paper. Besides, challenges of mobility in the RPL‐based routing protocol mechanism in IoMT for future studies. [ABSTRACT FROM AUTHOR]

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved. [ABSTRACT FROM AUTHOR]

Digital technology is an important tool to smooth the dual circulation. This paper empirically examines the impact of digital technology on the enterprise dual circulation and its mechanism based on the data of enterprises in ZGC from 2013 to 2019. It shows that digital technology can significantly promote enterprise dual circulation, and this conclusion still holds after a series of robustness tests. Mechanisms analyzed found that digital technology promotes enterprise dual circulation by unclogging blockages in the production, distribution, circulation, and consumption chain. Further research found that digital technology facilitates NSOEs more significantly than SOEs. Meanwhile, the application of e‐commerce, big data, cloud computing, mobile Internet, Internet finance, and Internet of Things promote more obviously compared with intelligent hardware. This paper provides micro‐empirical evidence that digital technologies smooth the dual circulation. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid‐based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering‐based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid‐based dynamic sink routing scenario. The significance of clustering‐based protocols for improving lifetime of the network is evaluated as case study. The clustering‐based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) has a significant impact on our daily lives as applications, services, devices, and industries become more intelligent. Artificial intelligence (AI) is expected to significantly influence machine learning training on IoT devices without data sharing. Federated learning (FL) is a distributed machine learning method used in many IoT smart devices; however, FL ensures IoT security and privacy. The systematic literature review (SLR) method is used in this paper to review recently published articles in the FL‐based IoT domain. We analyzed 39 papers that were published between 2018 and March 2022. According to the evaluation factors, the accuracy factor has a high percentage in the FL‐based IoT domain by 29%, and the epoch has 23%, the time has 18%, the energy consumption has 9%, the delay has 9%, the communication overhead has 6%, and the privacy has 6%. Finally, we discuss future research challenges and open issues in the context of FL‐based IoT. [ABSTRACT FROM AUTHOR]