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The localization is a very important task in many internet of things (IoT) based applications. Due to high requirements of communication cost and power consumption, however, existing localization methods are hard to apply for practical IoT applications with low power and narrow frequency. To answer this problem, in this paper, we propose a fully distributed low energy localization method by incorporating the distributed quantization aware least mean square (DQA-LMS) algorithm in a mobile diffusion narrowband IoT (NB-IoT) network. Each mobile network node in this case is assumed to use a low-power and low-resolution analog to digital converter (ADC). First, we address the performance analysis of the mobile networks in the presence of the quantized data. Second, we evaluate the localization performance of the proposed method with differently leveled quantization data and confirm that it can perform suitably even in the one-bit data case. Finally, we collate the localization performance of the Mobile DQA-LMS with the mobile distributed LMS (Mobile DLMS) and show that for all the quantized data cases, the performance of the mobile DQA-LMS is better than the conventional DLMS in terms of mean square deviation (MSD). [ABSTRACT FROM AUTHOR]

This article describes advancements towards the creation of the Internet of biological things and introduces a paper that introduces one such milestone. Biological systems, such as bees and dandelions, exhibit remarkable capabilities like efficient energy use and long-range seed dispersal, which current IoT and embedded systems cannot match. Recent advancements aim to bridge this gap by developing tiny, bio-integrated devices like mSAIL, a lightweight sensor capable of tracking monarch butterflies over long distances using innovative methods like neural network-based location estimation. This research represents a significant step toward creating a network of bio-inspired devices that could transform fields such as environmental monitoring and healthcare by harnessing the strengths of biological systems.

• This paper presents a lightweight and efficient spatial–temporal transformer network to learn collaboratively and efficiently to detect epileptic seizures. • A hierarchical FL framework is introduced to enable resource-efficient training of the detection network. • The proposed Fed-ESD mitigates the risk of a single point of failure by alleviating reliance on a centralized authority. Epilepsy is a predominant paroxysmal neurological disturbance that is usually recognized as the incidence of impulsive seizures rarely seen in medicine. Automatic detection of epileptic seizures from electroencephalogram (EEG) signals is viewed as an effective diagnosis of patients on the Internet of Medical Things (IoMT). To design a robust detection service in an IoMT environment, the EEG signals of different patients are collected from geographically distributed patients to a centralized server. However, this makes the patient's privacy prone to exposure and adds to the energy and communication costs. Also, the central server can be subject to malevolent attacks, resulting in non-efficient solutions. In this regard, for the first time, this paper presents a privacy-preserving federated learning framework for epileptic seizure detection (called Fed-ESD) from EEG signals in the fog-computing-based IoMT. A lightweight and efficient spatiotemporal transformer network is introduced to collaboratively learn spatial and temporal representations from the local data of each participant. The proposed Fed-ESD employs geographically situated fog nodes as local aggregators to enable sharing of location-based EEG signals for comparable IoMT applications. Moreover, a greedy method is introduced for deciding on the ideal fog node to be the coordinator node responsible for global aggregation during the training, thereby decreasing the reliance on the central server in the IoMT. Experimental evaluations demonstrate the efficiency of the proposed Fed-ESD in terms of detection performance, resource-efficiency, stability, and scalability for deployment in the IoMT. [ABSTRACT FROM AUTHOR]

Purpose: Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network. Design/methodology/approach: The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model. Findings: The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation. Originality/value: There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered. Highlights: Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm). [ABSTRACT FROM AUTHOR]

This paper investigates the problem of master-slave synchronization of stochastic quaternion-valued neural networks (SQVNNs) with mixed time-varying delays. A linear feedback controller is developed to explore the global synchronization of the proposed system by utilizing the complete information of the time-delay state. Sufficient conditions for synchronization of the proposed model are derived by constructing appropriate Lyapunov–Krasovskii functional by applying the master-slave synchronization method of master-slave and some integral inequality techniques. Finally, a corresponding numerical simulation is presented to demonstrate the accuracy of the theoretical results. This paper introduces a unique and efficient image encryption algorithm based on SQVNNs. This technique utilizes the solution set of SQVNNs to generate the high-level randomness secret keys to encrypt the source image. Finally, we conclude that the algorithm yields a source image cipher with excellent diffusion and confusion properties. A few test clinical images are utilized to show the validity of the proposed method. Several performance analyses show that the proposed algorithm for image encryption gives an efficient and secure way to deal with the Internet of Health Things (IoHT). [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyze the sustainable development of e-commerce. Design/methodology/approach: This paper proposes to analyze the development of rural e-commerce based on the perspective of big data of the Internet of things, in order to achieve the purpose of the development of e-commerce in China. In the analysis, this paper selects the data envelopment analysis (DEA) model for analysis and filters the parameters of the model to improve the accuracy of the model. Findings: The experimental results of this paper show that various efficiency values of the improved model have decreased, and the scale efficiency has decreased by 0.319 on average. From the decline of technical efficiency, pure technical efficiency and scale efficiency of rural e-commerce in each province after excluding environmental variables, it can be concluded that environmental variables have a greater impact on the development efficiency of rural e-commerce. Originality/value: In the analysis, this paper selects the DEA model for analysis and filters the parameters of the model to improve the accuracy of the model. [ABSTRACT FROM AUTHOR]

This paper describes a hybrid binary frequency-phase shift keying (BF-PSK) receiver architecture designed with a technique involving both the received signal frequency and phase for low-power operation with relatively high data rate. The method enables the demodulation of the incoming signal without synchronization requirements, which reduces the design complexity and power consumption. The architecture allows programmable data rates and channel bandwidths according to application-specific needs. A novel low-noise amplifier architecture is introduced in this paper as well. The Medical Implant Communication System (MICS) band receiver was designed and fabricated in a standard 65nm CMOS technology, and the measurement results demonstrate the feasibility of this architecture. As a proof-of-concept, it operates with a 416 MHz carrier frequency at a state-of-the-art data rate for sub-milliwatt receivers of 10 Mbps, while consuming $610~\mu \text{W}$ from a 1 V supply. [ABSTRACT FROM AUTHOR]

• Proposing a new deep learning model for accurately detecting cyber threats to extract a feature representation and learn temporal information from cybersecurity data sequences. • Decreasing the learning rate of the Adam optimizer exponentially to improve its exploitation operator when it cannot improve in the validation loss within several epochs. • Evaluating the proposed model's performance, DeepAK-IoT uses three datasets, two of which are aggregated using heterogeneous data sources, to assess its ability to detect the malicious code that can threaten IoT sources. • Refining DeepAK-IoT provides greater accuracy for the validated datasets, superior to three contemporary models. Our daily lives have been profoundly changed over the past few years owing to the growing presence of the Internet of Things (IoT). Importantly, IoT makes our lives more convenient, simpler, and more efficient; however, gadgets are vulnerable to a wide variety of cyberattacks due to the lack of robust security mechanisms and hardware security support. This paper presents an alternative deep learning model known as DeepAK-IoT to detect cyberattacks against IoT devices. DeepAK-IoT uses three blocks as its foundation: the residual-based-spatial representation (RSR) block, the temporal representation block (TRB), and the detection block (DB). The RSR block uses five residual blocks to extract a feature representation from the output of the preceding layer. The four convolutional layers are connected in parallel with a skip connection within each block to avoid vanishing or exploding gradients. Then, the second block uses the extracted spatial representation to learn a temporal representation to detect cyber threats. The final block decides how to classify the input record. We evaluated the accuracy and generalization ability of DeepAK-IoT using three well-known public datasets: TON-IoT, Edge-IIoTset, and UNSW-NB15. The proposed model was compared to three state-of-the-art deep learning models to demonstrate its effectiveness in detecting cyber threats in IoT systems. According to the experimental results, DeepAK-IoT was found to be a powerful alternative model for managing cyber threats in IoT networks, as it provided 90.57% accuracy for TON IoT, 94.96% for Edge-IIoTset, and 98.41% for UNSW NB15. [ABSTRACT FROM AUTHOR]

This paper presents a new technique of radio frequency (RF) signal strength detection with a received signal strength indicator (RSSI) circuit which can be deployed in an internet-of-things (IoT) network. The proposed RSSI circuit is based on a direct conversion of RF to digital code indicating the signal strength. The direct conversion is achieved by the repeated switching of a rectifier’s output voltage using an ultra-low power comparator. A 5-bit programmable feedback circuit is used to correct detection inaccuracies. The RSSI circuit is implemented in a 65-nm CMOS process and consumes 6nW power. It has a linear dynamic range of 26dB and exhibits an error of ± 0.5dB with a wide bandwidth of 750MHz. A detailed analysis of the RSSI circuit is presented and verified with simulation and measurement results. The high detection accuracy with ultra-low power consumption of our RSSI circuit is favourable for IoT applications including localization, beamforming, hardware security and other low-power applications. [ABSTRACT FROM AUTHOR]

Appendable-block blockchain was established in an effort to standardize a blockchain architecture for heterogeneous Internet of Things devices. In current constructions, the main disadvantage lies in the full trust of gateways, which can launch coordinated and individual eclipse attacks against IoT sensing devices without being caught. This paper introduces Hieraledger, a novel commitment protocol for exposing malicious gateways in appendable-block blockchain constructions. To minimize the likelihood of eclipse attacks, each sensing device selects more than one gateway based on a novel reputation scoring model and the self-valuation of its privacy. To expose malicious gateways, our protocol employs fail-stop signatures thwarting signature forging, cryptographic hash functions, and a novel Merkle appendable tree. For each sensing device, we generate a resource-aware repudiable cryptographic proof of origin evidence. For gateway nodes, we generate a chain of commitment-based non-repudiable cryptographic proofs of delivery evidence. To demonstrate the security and robustness of our scheme, we analyze it under a solid theoretical security model. Based on our findings, our protocol enables sensing devices to seek arbitration and expose malicious gateways to the network. The present study is a further addition to the growing body of research into privacy preserving blockchain solutions for the Internet of Things [ABSTRACT FROM AUTHOR]

In this paper, we provide a methodology to evaluate the capacity of a Massive multiple-input multiple-output (MIMO) supported Internet of Things (IoT) system in which a large number of low cost low power IoT devices transmit and receive sporadic data. Numerous IoT devices are supported by a single cell Massive MIMO base station (BS) with maximum-ratio (MR) processing. Orthogonal reference signals (RSs) or pilots are assigned randomly to all the IoT devices for channel estimation purpose. The number of simultaneously active IoT devices follows Poisson distribution. Due to the tremendous number of IoT devices, orthogonal RSs are heavily reused, which severely degrades the receiver signal quality. One of the most important performance criteria for this kind of system is the blocking probability which shows the percentage of the outage IoT devices, and how we maintain the low blocking probability while supporting all the IoT devices simultaneously is particularly important. Due to RS reuse, we can divide IoT devices into two groups based on their interference levels. We provide detailed theoretical analyses, and show that the blocking primarily happens to the group with higher interference level. Increasing the number of service antennas and/or reducing the number of IoT devices can help to improve the performance of the blocking probability, however there is a regime in which the parameter adjustment helps little to improve the performance. Based on these factors, we provide a useful algorithm to improve the performance of blocking probability. A number of simulation results are also provided to validate the theoretical analysis. [ABSTRACT FROM AUTHOR]

• Providing AI-enabled IoT system in healthcare monitoring and control. • Adjusting the depth of anesthesia in surgery by automatically infusion. • Designing an adaptive control system using a robust control method and fuzzy system. • Employing fuzzy learning to provide an intelligent estimator for patient model uncertainties. • Remote tuning of drug infusion through network channels. Smart remote patient monitoring and early disease diagnosis systems have made huge progresses after the introduction of Internet of Things (IoT) and Artificial Intelligence (AI) concepts. This paper proposes an AI-enabled IoT system to monitor and adjust the depth of anesthesia via network channels. More precisely, fuzzy learning systems are employed to develop a control system for the depth of anesthesia in surgeries. This scheme is composed of variable structure control and adaptive type-II fuzzy systems. Therefore, the controller is adaptive and robust to any perturbations and disturbances that may happen during a patient's surgery. The adaptive type-II fuzzy system is designed as an intelligent online estimator to approximate patient model uncertainties. This estimation helps in boosting the performance of the variable structure control system. An artificial neuron is also designed to reduce chattering for the proposed control system. The designed control system can efficiently adjust the anesthesia drug infusion rate and regulate the Bispectral index. The networked structure of the proposed system makes remote tuning of drug infusion possible. Performance of the designed controller is evaluated on several patient models. Simulation results confirm the validity and effectiveness of the proposed remote drug delivery system. [ABSTRACT FROM AUTHOR]

• We propose a verifiable way to judge whether cloud mining pools are honest. This method enables fast verification without being affected by the performance limitations of IoT devices, while also helping IoT devices to select high-performance cloud mining pools in BCoT. • By implementing the additive homomorphic ElGamal cryptosystem, we not only achieve privacy protection in data transmission but also utilize a semi-honest cloud server to reduce the computational overhead of resource-constrained IoT devices. • We design the scheme in detail and analyzed the correctness and security of the scheme. Simultaneously, after the performance analysis and comparison with related schemes, our scheme has higher efficiency in the stage of aggregation and encryption and decryption. Blockchain of Things (BCoT), as a new paradigm that incorporates blockchain with the Internet of Things (IoT), can address the privacy and extensibility issues in IoT systems and effectively enhance the data management of IoT devices. Asmost of theIoT devices are resource-constrained in terms of computing power, storage capacity, and energy consumption. Meanwhile,the enormous computational consumption in the consensus process of the blockchain makes it difficult to integrate with IoT closely and exert the great potential of BCoT. latterly, the "cloud mining" mechanism has been proposed to address these issues, however,some potential problems still exist, such as data privacy and dishonest cloud mining pools, etc. In this paper, we propose a v erifiable and p rivacy- p reserving c loud m ining p ool s election scheme in BCoT (namely , VPP-CMPS). To obtain a better solution for cloud mining pool selection, we implement an improved time-locked puzzles technique to identify dishonest mining pools and perform the authenticity for mining pool selection.Afterwards,we obtain the privacy protection of the data during the entire selection process by adopting additively homomorphic ElGamal cryptosystem. Meanwhile, considering the resource-constrained case in IoT devices, we use a semi-honest cloud server to cooperate in dealing with the selection procedure, thereby reducing the computational overhead of IoT devices. Experimental results indicate that our scheme is more efficient and provides less computational cost. [ABSTRACT FROM AUTHOR]

• A novel lightweight DL technique is presented for fine-tuning lung infection segmentation. • The down-sampling module is innovatively designed to effectively capture lesion features from cross-domain CT images. • A novel contextual enrichment module is introduced to empower the network to capture the combinations of static. • A novel federated periodic client selection protocol is proposed to select the training participants fairly and periodically. The automatic segmentation of COVID-19 pneumonia from a computerized tomography (CT) scan has become a major interest for scholars in developing a powerful diagnostic framework in the Internet of Medical Things (IoMT). Federated deep learning (FDL) is considered a promising approach for efficient and cooperative training from multi-institutional image data. However, the nonindependent and identically distributed (Non-IID) data from health care remain a remarkable challenge, limiting the applicability of FDL in the real world. The variability in features incurred by different scanning protocols, scanners, or acquisition parameters produces the learning drift phenomena during the training, which impairs both the training speed and segmentation performance of the model. This paper proposes a novel FDL approach for reliable and efficient multi-institutional COVID-19 segmentation, called MIC-Net. MIC-Net consists of three main building modules: the down-sampler, context enrichment (CE) module, and up-sampler. The down-sampler was designed to effectively learn both local and global representations from input CT scans by combining the advantages of lightweight convolutional and attention modules. The contextual enrichment (CE) module is introduced to enable the network to capture the contextual representation that can be later exploited to enrich the semantic knowledge of the up-sampler through skip connections. To further tackle the inter-site heterogeneity within the model, the approach uses an adaptive and switchable normalization (ASN) to adaptively choose the best normalization strategy according to the underlying data. A novel federated periodic selection protocol (FED-PCS) is proposed to fairly select the training participants according to their resource state, data quality, and loss of a local model. The results of an experimental evaluation of MIC-Net on three publicly available data sets show its robust performance, with an average dice score of 88.90% and an average surface dice of 87.53%. [ABSTRACT FROM AUTHOR]

Purpose: The concept of e-learning is essential in employee education since it provides different ways to develop employees' knowledge, skills and attitudes using modern technologies. E-learning has been overgrowing in employee education because learning can be held anytime and anywhere. In order to succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address the factors affecting its success. This paper aims to test the role of internet of Things (IoT)-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework. Design/methodology/approach: E-learning systems receive ever-increasing attention in academia, business and public administration. With the development of e-learning, employee education has also benefited from its capacities in various fields. To succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address its success. The proposing of Information and Communications Technology (ICT)-based technologies such as the IoT, cloud, etc., in e-learning, can help transform education. Therefore, this paper aims to test the role of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework. The research model and the data collected from the questionnaires have been analyzed via Smart PLS 3.2. This study has utilized the SEM to evaluate the causal model's reliability and validity based on measurement. According to the literature in this study, a framework has been proposed that examines the impact of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on employees' learning programs' success. Findings: The results have shown that IoT-based systems, cloud-based services, virtual classes and evaluation tools are four significant factors affecting attitude, content management and creativity. The results have also shown that attitude, content management and creativity are three significant factors affecting employees' learning programs' success. The factors above are considered critical in explaining the success of employees' e-learning programs, but, as far as we know, there has been no study in which all these factors were demonstrated together. Practical implications: From a practical viewpoint, the statistical outcomes support the important role of the following factors: IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity. Henceforth, aspects relating to these factors got the attention of any organization to develop e-learning processes. Originality/value: This research will contribute to the literature related to employees' e-learning programs' success by integrating all the mentioned variables. As far as we know, it is the first study to test these variables in Iran. [ABSTRACT FROM AUTHOR]

Due to the development of microprocessor technology, there are more than 20 billion sensor-based devices connected to the Internet of Things (IoT) to monitor physical phenomena and events. To reduce the energy used by idle listening, a low-duty cycle is often used in an event-sparse wireless sensor network. However, low-duty cycles bring large end-to-end delays. In this paper, a listen interval adaptive adjustment (LIAA) scheme is proposed to adjust the listen interval (LI) of a node to reduce end-to-end delays while maintaining the long lifetime of a network. The key idea is to make full use of the energy consumption imbalance in a network, to allow nodes away from the sink to use the residual energy to add listen intervals. The LIAA scheme has 3 sub-strategies. One is the basic add listen interval (BALI) strategy, in which the parent node adds listen intervals in the fixed active slots of its child nodes. Another strategy is the consecutive listen (CL) scheme, which is based on BALI, and listen intervals are added consecutively. The third strategy is the random add listen interval (RALI) scheme, which uses the idea of randomness to add listen intervals. The extensive theoretical analysis and experimental results show that the LIAA scheme proposed in this paper has better performance. Compared with the traditional scheme, the delays in the BALI scheme, CL scheme and RALI scheme were reduced by 24.03%, 23.45% and 39.41%, respectively, while the lifetime of the network was maintained. [ABSTRACT FROM AUTHOR]

Purpose: Security is the most important issue in Internet of Things (IoT)-based smart cities and blockchain (BC). So, the present paper aims to detect and organize the literature regarding security in the IoT-based smart cities and BC context. It also proposes an agenda for future research. Therefore, the authors did a statistical review of security in IoT and BC in smart cities. The present investigation aims to determine the principal challenges and disturbances in IoT because of the BC adoption, the central BC applications in IoT-based smart cities and the BC future in IoT-based smart cities. Design/methodology/approach: IoT) has a notable influence on modernizing and transforming the society and industry for knowledge digitizing. Therefore, it may be perceived and operated in real time. The IoT is undergoing exponential development in industry and investigation. Still, it contains some security and privacy susceptibilities. Naturally, the research community pays attention to the security and privacy of the IoT. Also, the academic community has put a significant focus on BC as a new security project. In the present paper, the significant mechanisms and investigations in BC ground have been checked out systematically because of the significance of security in the IoT and BC in smart cities. Electronic databases were used to search for keywords. Totally, based on different filters, 131 papers have been gained, and 17 related articles have been obtained and analyzed. The security mechanisms of BC in IoT-based smart cities have been ranked into three main categories as follows, smart health care, smart home and smart agriculture. Findings: The findings showed that BC's distinctive technical aspects might impressively find a solution for privacy and security problems encountering the IoT-based smart cities development. They also supply distributed storage, transparency, trust and other IoT support to form a valid, impressive and secure distributed IoT network and provide a beneficial guarantee for IoT-based smart city users' security and privacy. Research limitations/implications: The present investigation aims to be comprehensive, but some restrictions were also observed. Owing to the use of some filters for selecting the original papers, some complete works may be excluded. Besides, inspecting the total investigations on the security topic in BC and the IoT-based smart cities is infeasible. Albeit, the authors attempt to introduce a complete inspection of the security challenges in BC and the IoT-based smart cities. BC includes significant progress and innovation in the IoT-based smart cities' security domain as new technology. Still, it contains some deficiencies as well. Investigators actively encounter the challenges and bring up persistent innovation and inspection of related technologies in the vision of the issues available in diverse application scenarios. Practical implications: The use of BC technology in finding a solution for the security issues of the IoT-based smart cities is a research hotspot. There is numerable literature with data and theoretical support despite the suggestion of numerous relevant opinions. Therefore, this paper offers insights into how findings may guide practitioners and researchers in developing appropriate security systems dependent upon the features of IoT-based smart city systems and BC. This paper may also stimulate further investigation on the challenge of security in BC and IoT-based smart cities. The outcomes will be of great value for scholars and may supply sights into future investigation grounds in the present field. Originality/value: As the authors state according to their knowledge, it is the first work using security challenges on BC and IoT-based smart cities. The literature review shows that few papers discuss how solving security issues in the IoT-based smart cities can benefit from the BC. The investigation suggests a literature review on the topic, recommending some thoughts on using security tools in the IoT-based smart cities. The present investigation helps organizations plan to integrate IoT and BC to detect the areas to focus. It also assists in better resource planning for the successful execution of smart technologies in their supply chains. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips. [ABSTRACT FROM AUTHOR]

• An AI algorithm for data enhanced encryption at the end and the intermediate nodes of autonomous IoT systems is proposed. • An AI access strategy is designed for reducing the calculation cost of data encryption. • A data shared matrix is proposed to share the encrypted data to achieve the (k, n) threshold strategy. Aiming at the security issues during the multi-types data storage and data transmission in autonomous Internet of Things (IoT) systems, this paper proposes an AI algorithm for data enhanced encryption used in the ends and the intermediate nodes of IoTs. The algorithm in this paper first constructs a three-dimensional Arnold transformation matrix for data unit value encryption in the end of IoTs, and designs a quantum logic intelligent mapping that effectively diffuses the encrypted data units to reduce the linear correlation of the image data and to improve the security performance of IoT edge data. Furthermore, the algorithm designs an AI access strategy for scrambling sequence nodes and builds a random-access route for the elements of the scrambling sequence which can reduce the calculation cost and improve the operating efficiency of IoT system in the ends and intermediate nodes. Finally, the data shared matrix is used to share the encrypted data to achieve the (k, n) threshold strategy. Experimental results prove that the algorithm has high plaintext and key sensitivity and can effectively resist brute force attacks, statistical analysis and differential attacks. The algorithm in this paper provides an AI solution for data security encryption in the ends and the intermediate nodes of autonomous IoT systems. [ABSTRACT FROM AUTHOR]

In a previous paper (Kim, 2019), an analytical framework based on the constraint satisfaction problems was proposed to reveal the characteristics of households using event logs from smart door lock systems. This work provides a more rigorous justification for the previous approach. This paper proposes a novel parameter estimation method called the maximum feasibility estimation (MFE). The MFE does not rely on any assumption about the parametric family of probability densities from which a random observation is drawn. Instead, we assume that constraints are imposed on observations and that some of the constraints are a function of a parameter of interest. The proposed estimator maximizes the feasible region, a set of all possible observations that satisfy those constraints. The method proposed is validated using synthetic data as well as real streaming event log data. [ABSTRACT FROM AUTHOR]

Due to the low complexity in arithmetic unit design, stochastic computing (SC) has attracted considerable interest to implement Artificial Neural Networks (ANNs) for resources-limited applications, because ANNs must usually perform a large number of arithmetic operations. To attain a high computation accuracy in an SC-based ANN, extended stochastic logic is utilized together with standard SC units and thus, a stochastic divider is required to perform the conversion between these logic representations. However, the conventional divider incurs in a large computation latency, so limits an SC implementation for ANNs used in applications needing high performance. Therefore, there is a need to design fast stochastic dividers for SC-based ANNs. Recent works (e.g., a binary searching and triple modular redundancy (BS-TMR) based stochastic divider) are targeting a reduction in computation latency, while keeping the same accuracy compared with the traditional design. However, this divider still requires $N$ iterations to deal with $2^{N}$ -bit stochastic sequences, and thus the latency increases in proportion to the sequence length. In this paper, a decimal searching and TMR (DS-TMR) based stochastic divider is initially proposed to further reduce the computation latency; it only requires two iterations to calculate the quotient, so regardless of the sequence length. Moreover, a trade-off design between accuracy and hardware is also presented. An SC-based Multi-Layer Perceptron (MLP) is then considered to show the effectiveness of the proposed dividers over current designs. Results show that when utilizing the proposed dividers, the MLP achieves the lowest computation latency while keeping the same classification accuracy; although incurring in an area increase, the overhead due to the proposed dividers is low over the entire MLP. When using as combined metric for both hardware design and computation complexity the product of the implementation area, latency, power and number of clock cycles, the proposed designs are also shown to be superior to the SC-based MLPs (at the same level of accuracy) employing other dividers found in the technical literature as well as the commonly used 32-bit floating point implementation. [ABSTRACT FROM AUTHOR]

As the basis of many artificial intelligence tasks, text similarity measurement has received extensive attention in current studies. However, few of them focus on the resource-limited scenes (i.e., limited computational resources and few training datasets), which are becoming increasingly popular and challenging with the development of the Internet of Things. Worse still, popular methods such as the deep-neural-network-based methods may lose their power in such scenes, since they typically require considerable computational resources. As for most current traditional methods, they also have issues of not effectively exploiting the semantic information in the sentences. As an alternative, this paper proposes a lightweight and semantically rich text similarity measurement model named the TES-TK model. In this model, a sentence is first transformed into a tree structure called TES-Tree with the integration of syntactic information, semantic knowledge, and topic distribution, aiming to comprehensively represent the multidimensional semantics of sentences. Afterward, a modified tree kernel model is designed to calculate the similarity between each pair of TES-Trees. In this way, the similarity score between the two related sentences can be retrieved. Experiments on 19 public benchmark datasets (STS2012–2015) demonstrate that the proposed approach exhibits significantly better performance than the compared eight peer methods on most datasets. Especially in resource-limited scenes, our approach achieved highly competitive results compared with the latest methods, such as BERT. [ABSTRACT FROM AUTHOR]

With the privacy protection increasingly being concerned, Data centralization often heavily causes a big risk of privacy protection, gradually, there is a prevailing trend to enhance the security performance by means of data decentralization, above all, for health care internet of things (IoT) data. Meanwhile, Federated learning has obvious privacy advantages compared to data center training on protecting privacy data. For this reason, a novel framework based on federated learning is presented in this paper, which is suitable for private and decentralized data sets, such as big data in healthy Internet of Things. Specifically, the main work of the puts forward framework includes: (1) Multi-center data collection of healthy Internet of Things. (2) healthy data analysis of Internet of Things. (3) privacy protection method for data of healthy Internet of Things. Finally, related experiments show that the proposed method is feasible, and compared with the traditional methods, it has significantly improved the performance in Quality of Service (QoS) and IoUs indicator. [ABSTRACT FROM AUTHOR]

[Display omitted] • The scheme does not require users' original data to be exchanged and relies on homomorphic encryption to ensure data security during processing and transmission. • Each operation in the scheme is performed using encrypted data. To ensure that the normal business of trusted institutions is not affected, the amount of calculation is kept to a minimum. • The system's security, execution efficiency, and final result accuracy are evaluated using both theoretical and experimental methods. In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants' limited resources, processing and analyzing ciphertext data presents a significant challenge. As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant's resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant's computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme's security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme's universality. [ABSTRACT FROM AUTHOR]

This paper presents an integrated ultra-low-power (ULP) wireless sensor system-on-chip (SoC) that can be used for voltage sensing in both Internet of Things applications and bio-potential monitoring. In order to increase the energy efficiency of the analog front-end (AFE), we propose a noise and power efficient push-pull low noise instrumentation amplifier (LNIA) with a built-in ripple reduction loop based on capacitor reuse. A low-power ISM-band harmonic injection locked on-off-keying transmitter (OOK-TX) is also implemented for energy efficient wireless connectivity. Circuit implementations, design considerations, and detailed analysis are presented to improve the overall energy efficiencies of the SoC including the AFE, TX and, the power management unit. The proposed ULP-SoC is fabricated in 130 nm CMOS technology with a total area of 1.92 mm2. The total power consumption of the proposed system-on-chip is 197.1 μW which is one of the lowest among state-of-the-art wireless sensor SoC. [ABSTRACT FROM AUTHOR]

• To detect drifts in data stream when noise presents. • A novel tolerant drift detection method is designed to detect and validate. • A sparse sliding window strategy is proposed to reduce false drift detection. • Our method outperforms the existing six established drift detection methods in both accuracy and timeliness. Drift detection methods identify changes in data streams. Such changes are called concept drifts. Existing drift detection methods often assume that the input is a noise-free data stream. However, in real world applications, for example, data streams generating from internet of things are normally contaminated with noise. (noise, i.e. class noise and/or attribute noise). In this paper, we propose a Noise Tolerant Drift Detection Method (NTDDM), which is based on two-step detection and validation function to detect drifts, and filters out the false drifts caused by the noise. The NTDDM is compared with six well-known drift detection methods and tested on four benchmarks having different levels. Three performance indicators are proposed to determine whether the drift detection is made within a reasonable time, and the length of time to the known drift starting point. The comparative studies demonstrate that NTDDM outperforms the existing methods, over these performance indicators. Our proposed method has achieved a statistically significant improvement on drift detection compared to the methods in experiment. The proposed NTDDM makes it possible to efficiently and effectively detect drift in a noisy data stream. [ABSTRACT FROM AUTHOR]

With the continuous expansion of Internet of Things (IoT) devices, edge computing mode has emerged in recent years to overcome the shortcomings of traditional cloud computing mode, such as high delay, network congestion, and large resource consumption. Thus, edge-thing systems will replace the classic cloud-thing/cloud-edge-thing systems and become mainstream gradually, where IoT devices can offload their tasks to neighboring edge nodes. A common problem is how to utilize edge computing resources. For the sake of fairness, double auction can be used in the edge-thing system to achieve an effective resource allocation and pricing mechanism. Due to the lack of third-party management agencies and mutual distrust between nodes, in our edge-thing systems, we introduce blockchains to prevent malicious nodes from tampering with transaction records and smart contracts to act as an auctioneer to realize resources auction. Since the auction results stored in this blockchain-based system are transparent, they are threatened with inference attacks. Thus in this paper, we design a differentially private combinatorial double auction mechanism by exploring the exponential mechanism such that maximizing the revenue of edge computing platform, in which each IoT device requests a resource bundle and edge nodes compete with each other to provide resources. It can not only guarantee approximate truthfulness and high revenue, but also ensure privacy security. Through necessary theoretical analysis and numerical simulations, the effectiveness of our proposed mechanisms can be validated. [ABSTRACT FROM AUTHOR]

Despite that the emerging internet of things (IoT) increases the chance for users to interact with things, generating massive context data, the ground-truth user-thing interaction data is still quite sparse compared with the enormous numbers of users and things. Current existing things recommendation methods mostly adopt machine learning or deep learning (e.g., matrix factorization or neural network) as their basic models, which continuously results in that the data sparsity has a great impact on their performance. Although they integrate different contexts to alleviate the problem, the real effect is rather limited. To the end, in the paper, we study how to achieve better recommendation results in the case of the data sparsity. Along this line, the very critical challenge is how to learn user preferences without relying too much on big data. As a solution, we first build an interaction event experience graph (IEEG) from the user interaction data. Secondly, we develop a Non-Axiomatic Logic-based learning framework for novel things correlations, named NALC, running over IEEG, to mine the user-thing relationship. We measure the user-user relationship and the thing-thing relationship from real-time contexts. Furthermore, we fuse the user-user, the thing-thing and the user-thing relationships based on a product rule, to model the recommender. Finally, we evaluate the performance of our proposed model on the real-world dataset and demonstrate that the proposed model outperforms existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

The continuous emergence of new malware has been a severe threat to Industrial Internet of Things (IIoT), while identifying malware through detecting malicious traffic in encrypted, drift, and imbalanced traffic streams is a challenge. This paper proposes an approach based on adaptive online analysis to accurately determine the families of malware by analyzing traffic streams which are encrypted, drift, and imbalanced. This approach is based on Improved Adaptive Random Forests (IARF), to obtain the ability of adaptive update of parameters when processing new types of malware traffic in traffic streams and being sensitive to families of malware with few samples in imbalanced traffic. We build a prototype of this approach and evaluate the performance through experiments. The experiments are based on a mixed dataset composed of data from malware-traffic-analysis.net, Lastline Inc, MCFP dataset, and CTU-13 dataset. In addition, our approach is also compared with three state-of-the-art methods. The results of the experiments show that we have obtained a 99.66% F1-score in the classification of malware families, and our classifier also performs better than the other classifiers. [ABSTRACT FROM AUTHOR]

Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL-IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees. [ABSTRACT FROM AUTHOR]

Obtaining location information of the multi-sensor internet of things (IoT) is a fundamental requirement. But, two kinds of the incomplete measurement data enhance the difficulty of positioning: 1) The location information is partially missing. 2) The errors of the measurement. Furthermore, the complex environmental impact and the characters of the measurement methods make the error model more changeable at the same time. This paper tries to study a universal method to address these problems, through combining IoT positioning and affinity graph assessment with arbitrary model of the measurement errors or the missing measurement data. First of all, the relationship between the graph signal and the combinatorial graph Laplacian matrix is constructed to link the graph topology learning and the IoT localization problem. Later, the Gaussian mixture model is applied to describe the measurement errors as a general model. Then, the calculations of the graph signal, Laplacian matrix and hyper parameters are obtained via variational Bayesian inference and message passing. The numerical results show the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCs) is a widely applicable and inexpensive data-obtaining method that leverages mobile devices to sense and report data without deploying sensors. The rapid development of the Integration of IoT systems, as well as the widespread use of communication satellites, make MCs further develop and receive extensive attention from researchers. One key issue is how to ensure the reliability of the reported data to avoid the presence of malicious participants leading to poor IoT service quality. In this paper, we proposed an Active and Trace Back based Trust Management (ATBTM) algorithm to evaluate the trust of participants and handle malicious participants. The main innovations are: (1) An active trust evaluation approach for MCs is proposed, which uses an Unmanned Aerial Vehicle (UAV) to collect data as a baseline to verify the reliability of the data reported by participants. Then, the data reported by high reputation participants can also be used as a sub-baseline to verify the trust of some participants. (2) A traceback based trust evaluation method is also proposed. In this approach, some reliable devices provide historical sensing data that has been collected but not reported. Then the system compares it to real data with corresponding timestamps to evaluate the trust of other participants. Sufficient theoretical analysis and experimental results demonstrate that the proposed ATBTM framework can effectively identify the malicious workers, and conquer the drawbacks of lacking trust evaluation method in existed MCs. [ABSTRACT FROM AUTHOR]

• The Internet of Vehicle (IoV) is an emerging technology for the development of future smart cities. • The world climate institution has reported that the carbon emission from transportation system is one-fifth of global carbon dioxide with a sum of around 24% of energy. The communication between EV and Roadside Unit (RSU), and the continuous computational power required to support IoV system is also energy harvesting. • In this paper, we propose a decentralized trust management solution for IoV systems to reduce both carbon print and offload the computation power required. • Our solution resides in developing a Digital Twin of the vehicle on an intelligent edge environment to simulate the physical vehicle and process the required data processing. The Internet of Vehicle (IoV) is an emerging technology for the development of future smart cities. With the fast and exponential growing rate of Internet of Things (IoT), the smart transportation field is ushering in a revolutionary advancement. Smart transportation systems facilitate better informed, more coordinated, and smarter use of transport networks, with the use of advanced information and communication technologies applied to vehicles to help improve traffic management, minimize congestion, improve safety, and ultimately provide a more intelligent use of transport networks. Smart transportation is an integral part of modern-day smart city projects. However, the world climate institution has reported that carbon emissions from the overall transportation system accounts for one-fifth of global carbon dioxide with a sum of around 24% of energy. Electric vehicles (EV) represent a solution for this issue, yet, it is not sustainable. The communication between EVs and a roadside unit (RSU), and the continuous computational power required to support an IoV system also requires a reliance on energy harvesting. With this in mind, in this paper, we propose a decentralized trust management solution for IoV systems to reduce both carbon footprint and offload the computation power required. Our solution resides in developing the digital twin of vehicles on an intelligent edge environment to simulate the physical vehicle and handle the required data processing. Also, we implement a smart contract model for fast, secure, and sustainable on-road battery recharging between EVs. [ABSTRACT FROM AUTHOR]

Network intrusion detection is one of the most important components of mobile networks security. In recent years, the application of neural networks has been very popular in network intrusion detection. However, due to limited resources of IoT devices, fast detection of the intrusion requires a high accuracy neural network with a lightweight and efficient architecture. Therefore, the conventional architectures of neural networks are not suitable for intrusion detection in IoT devices due to the use of a large number of parameters in these models concerning the limited processing resources in IoT devices. This paper presents a new and lightweight architecture based on Parallel Deep Auto-Encoder (PDAE) that uses both locally and surrounding information around individual values in the feature vector. This type of separation of features allows us to increase the accuracy of the model while greatly reducing the number of parameters, memory footprint, and the need for processing power. The effectiveness of the proposed model is evaluated using KDDCup99, CICIDS2017, and UNSW-NB15 datasets and the results shows the superiority of the proposed model over the state-of-the-art algorithms in terms of both accuracy and performance. [ABSTRACT FROM AUTHOR]

• A generalized Student's t-based kernel function, and a new kernel adaptive filtering (KAF) algorithm named generalized Student's t kernel adaptive filter (GStKAF) are designed under the KMPE error criterion for indoor positioning under the IoT framework. • The L1-norm is proposed as the penalty to embed into the GStKAF and the resulting sparse GStKAF (SGStKAF) can provide more compact size of the neural networks. • Three experimental results show that the proposed SGStKAF has good robustness against the mixed noise consisting of abrupt noise and Gaussian noise while maintaining the high accuracy performance. The mixed noise such as Gaussian noise together with the abrupt noise widely exists in the indoor environment, which always leads to the problem of performance degradation of the positioning system under the Internet of Things (IoT). In this paper, a novel kernel function named generalized Student's t kernel (GSt) and a resulting sparse generalized Student's t kernel adaptive filter (SGStKAF) is proposed to attack this problem. The proposed SGStKAF utilizes the kernel mean p -power error criterion (KMPE) with the L 1 -norm penalty. The proposed SGStKAF has three significant features. Firstly, the generalized Student's t kernel can suppress the abrupt noise effectively. Secondly, the L 1 -norm penalty guarantees that the fixed-point sub-iteration is available so that the more precise solution can be obtained in a few iterations. At last, a sparse structure of neural networks for the implementation of the proposed method can also be obtained via the L 1 constraint. Three experiments and comparisons are carried out to prove the effectiveness of the proposed positioning framework in terms of accuracy and robustness in both the simulation situation and the real-world indoor environments. [ABSTRACT FROM AUTHOR]

• Reviewed 182 articles on BCT's application in IoMTs contexts. • Provided a summary framework for the function of BCT within an IoMTs environment. • A road map was provided for promoting the adoption of BCT in IoMTs settings. • Successful deployment and adoption of BCT in an IoMTs setting were discussed. The Internet of Medical Things (IoMTs) has rapidly gained popularity recently, and the need for self-health assessment and the resulting reduction in treatment waiting periods have sped up the usage of IoMTs. Unfortunately, the growing usage of IoMTs has also brought up issues that have unintended effects, including a lack of data management, an unsafe network environment, privacy and security concerns. To address the problems with IoMT applications, distributed ledger technology, which forms the basis of blockchain technology (BCT), has emerged as a practical solution. The utility of BCT to support IoMT-based settings has been demonstrated by academic research. However, no analysis of the literature has been performed to comprehend BCT performance in IoMT scenarios. Therefore, the present study offers a thorough literature review of 182 studies on the use of BCT in an IoMT environment, drawn from 70 pioneering journals included in the WoS database. It also outlines recommendations for future research areas. This study adds to the body of literature in three main ways using a mix of bibliographic and content analysis. It starts out by outlining the structure of how BCT works in an IoMT setting. Secondly, it provides a roadmap that takes into account the factors that might aid in BCT proliferation in IoMT situations. Thirdly, the paper outlines the prerequisites for BCT's effective adoption and use in an IoMT environment. The implications of the recognized clusters and the factors influencing the properties and outcomes of BCT are covered in the article's conclusion. This is the first effort to investigate BCT's function within the context of the IoMT environment; this has become crucial since BCT has developed popularity in IoMT contexts. The article highlights many aspects that may help to improve comprehension of this important issue by utilizing the provided outline as a guide and identifying major knowledge gaps. [ABSTRACT FROM AUTHOR]

The time series database is a specialized type of database specifically designed for storing and analyzing time series data. Compression of time series data is crucial for its performance. However, efficiently compressing time series data, particularly floating-point data, remains challenging. Existing compression algorithms are efficient for only a limited range of data patterns, indicating a lack of self-adaptation. In this paper, we propose an effective and A daptive lossless F loating-point C ompression algorithm AFC for time series databases. We devise four unique compression strategies, and based on the data patterns, AFC dynamically selects the appropriate strategy. These strategies handle data compression for diverse data patterns, enhancing the compression ratio and efficiency. The most suitable strategy is employed to achieve an optimal compression ratio. We compared our AFC algorithm with four state-of-the-art compression algorithms, namely Gorilla, FPC, TSXor, and Chimp, as well as various general-purpose compression algorithms such as LZ4 and Snappy. Experimental results demonstrate that our algorithm achieves an improvement of at least 20% in compression ratio and even up to 100% on certain datasets. [ABSTRACT FROM AUTHOR]

Federated learning is a promising approach in the Internet of Things (IoT) that enables collaborative and distributed machine learning among massive IoT devices without sharing private data, thereby constructing smart IoT applications. However, traditional federated learning approaches are unable to monitor the local training process of devices. Some malicious devices can exploit the vulnerability to conduct Byzantine attacks, which may potentially lead to the failure or compromise of shared global model. In this paper, we propose a new Byzantine-robust federated learning framework called rFedFW, which aims to achieve secure and trustable federated learning in the IoT. Specifically, we propose a dual filtering mechanism to identify and discard malicious gradients. Furthermore, we design an adaptive weight adjustment scheme that dynamically reduces the aggregated weight of potentially malicious gradients, ultimately achieving robust model aggregation. Additionally, we propose a dynamic clipping method to reduce the magnitude of various gradients, and we incorporate an additive model aggregation scheme with momentum to smooth the effects of local gradients and achieve efficient model aggregation. Extensive experimental results on various datasets demonstrate the effectiveness and robustness of rFedFW. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are considered a key paradigm to improve the coverage of beyond-fifth-generation (B5G) networks as signal reflectors. Moreover, device-to-device (D2D) communications and non-orthogonal multiple-access (NOMA) schemes will enable networks with a large number of devices for the Internet of Things. This paper analyzes the performance of the downlink of B5G network, where both a NOMA-empowered BS and a RIS support cellular communications co-existing with inband D2D transmissions. We derive approximate expressions of the outage probability, the cumulative distribution function (CDF), and the probability density function (PDF) of the signal-to-noise-plus-interference ratio (SINR) of the NOMA-connected users and the D2D receivers. We further derive similar expressions under orthogonal multiple access (OMA) transmission for comparison purpose. Then, we numerically compute the ergodic rate of each user for a given power allocation. The derived expressions are validated by extensive simulations. • Outage probability for RIS-Assisted NOMA-empowered system with D2D transmissions. • CDF and PDF of the SINR of downlink users and D2D receivers. • Achieved ergodic rate for the downlink users and D2D receivers. [ABSTRACT FROM AUTHOR]

Low-delay and low-cost communication are the main concerns of the wireless sensor-based Internet of Things (IoT), which is widely deployed in various kinds of applications. Adopting wake-up radio (WuR)-enabled wireless sensor nodes can effectively reduce delay, but the deployment costs of the network increase. In this paper, we propose a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for WuR-enabled wireless sensor networks (WSNs). Urgent data prefer to select the awake non-WuR node to forward data. If there is no available awake non-WuR node to forward data at the instant, it will awaken WuR nodes alternatively to maintain a relatively low delay. However, normal data are forwarded by the awake non-WuR node to reduce the number of deployed WuR nodes. Our theoretical analysis results demonstrate that the DDSR scheme can effectively reduce the deployment costs by 93.63%–98.91% while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the physical-layer security for an Internet of Things (IoT) relaying network employing non-orthogonal multiple access (NOMA) in the context of non-linear energy harvesting. In particular, a power-constrained source transmits its confidential information to a destination via an IoT device, which first decodes the received signal and then forwards the decoded data together with its own information using the NOMA technique to their respective destinations, while an eavesdropper is overhearing this transmission. Considering the scenario that both the power-constrained source and the IoT device are assumed to work under non-linear energy harvesting modes, and are capable of harvesting energy from a power beacon with a time switching protocol, both analytical and asymptotic expressions for secrecy outage probability as well as the analytical expression for the probability of strictly positive secrecy capacity are derived. In addition, those expressions are also verified with Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

The paper presents a comprehensive closed-form performance analysis framework for multihop communications over Weibull fading channels. This framework may be of interest in different applications in the contexts of beyond-5G (B5G) and Internet of Things (IoT) use cases. The analyzed scheme consists generally of multiple regenerative relays, and we also consider generalized high-order quadrature amplitude modulation (M -QAM) transmissions. To take into consideration the channel fading, we adopt the Weibull model for its largely flexible ability to cover different channel conditions in different application contexts. The end-to-end performance is evaluated in terms of outage probability, bit error probability (BER), symbol error probability (SER), block error rate (BLER), ergodic capacity, and energy efficiency (EE). For all the metrics, we present exact closed-form expressions—along with their asymptotic behavior—capitalizing on the powerful generalized hypergeometric functions. To illustrate the utility of the obtained analytical results, we derive two BER- and EE-optimal transmit power allocation strategies, and we discuss the resulting performance gains. The exactness of our analysis is illustrated by numerical examples, and assessed via Monte-Carlo simulations for different system and channel parameters. Finally, as a secondary contribution, and noting the increasing popularity of single and bivariate Fox's H -function, we provide generalized Matlab codes for computing these functions which are of practical utility in many fields. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is commonly deployed with devices of limited power and computation capability. A centralized IoT architecture provides a simplified management for IoT system but brings redundancy by the unnecessary data traffic with a data center. A decentralized IoT reduces the cost on data traffic and is resilient to the single-point-of-failure. The blockchain technique has attracted a large amount of research, which is redeemed as a perspective of decentralized IoT system infrastructure. It also brings new privacy challenges for that the blockchain is a public ledger of all digital events executed and shared among all participants. The decentralized IoT system relies on the leader election deeply to implement the decentralized communications among the distributed nodes. The conventional leader election must have a centralized authority, contrasting to the decentralization. As an alternative, self-tallying type schemes have been proposed in the literature for decentralized systems. These schemes suffer from adaptive and abortive issues. Also, some additional factors should be considered, such as the availability of candidate nodes. If the candidate node is unavailable after the voting phase due to being offline or ongoing tasks, the next available candidate should be elected. To accommodate such a need, in this paper, we propose a new leader election paradigm called voluntary-tallying leader election, which achieves the core requirements such as ballet secrecy, voter privacy and the additional feature of voluntary-tallying. We formalize the system and security models for this new election paradigm and present a secure and practical construction. [ABSTRACT FROM AUTHOR]

With the number of Internet of Things (IoT) devices proliferating, the traffic volume of IoT-based attacks has shown a gradually increasing trend. The IoT botnet attack, which aims to commit real, efficient, and profitable cybercrimes, has become one of the most severe IoT threats. Applying traditional techniques to IoT is difficult due to its particular characteristics, such as resource-constrained devices, massive volumes of data, and real-time requirements. In this paper, we explore an adaptive online learning strategy for real-time IoT botnet attack detection. Furthermore, we operate the proposed adaptive strategy in conjunction with online ensemble learning. To evaluate the proposed strategy, we use real IoT traffic data, including benign traffic data and botnet traffic data infected by Mirai. In real-time IoT botnet attack detection, our experimental results demonstrate that the proposed adaptive online learning strategy achieves remarkable performance. [ABSTRACT FROM AUTHOR]

Under the power environment of Internet of things used in autonomous multi-robot systems, due to the influence of various actual environmental factors, the security protection method based on security partition and physical isolation of nodes is difficult to fully meet the information security protection requirements of the power system, and the network threats such as impersonation, eavesdropping, and dexterity on communication links are still difficult to avoid. In order to improve the security of node data transmission in autonomous multi-robot power systems, reducing network energy consumption has become the bottleneck of network application and development. In this paper, we propose an Improved Effective Clustering Algorithm (IECA) and transmission algorithm of power grid data based on autonomous multi-robot systems. The distance between nodes sending data to the next-hop node is equivalent to the energy model. The remaining energy in the cluster head node sending data in the next hop is recorded, and the remaining energy is subtracted from the equivalent energy to find a maximum value. The corresponding node of the maximum value is taken as the number of nodes According to the received node. Compared with the LEACH algorithm and GAF algorithm, this algorithm can not only extend the lifetime of nodes but also has more efficient performance. [ABSTRACT FROM AUTHOR]

In the field of network security, it is of great significance to accurately detect various types of Internet of Things (IoT) network intrusion attacks which launched by the attacker-controlled zombie hosts. In this paper, we propose a novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network (APSO-CNN). In particular, the PSO algorithm with change of inertia weight is used to adaptively optimize the structure parameters of one-dimensional CNN. The cross-entropy loss function value of the validation set, which is obtained from the first training of CNN, is taken as the fitness value of PSO. Especially, we define a new evaluation method that considers both the prediction probability assigned to each category and prediction label to compare the proposed APSO-CNN algorithm with CNN set parameters manually (R-CNN). Meanwhile, the comprehensive performance of proposed APSO-CNN and other three well known algorithms are compared in the five traditional evaluation indicators and the accuracy statistical characteristics of 10 times independent experiments. The simulation results show that the multi-type IoT network intrusion attack detection task based on APSO-CNN algorithm is effective and reliable. [ABSTRACT FROM AUTHOR]

Context-aware systems (CASs) have become increasingly prevalent in modern-day settings such as digital home and smart healthcare, primarily owing to the expansion of the Internet of Things (IoT). High-quality contexts play a vital role in the effectiveness of CASs, especially in applications involving multiple sensors. When multiple sensors provide identical information, utilizing various sources of context can enhance the dependability of said contexts through the provision of mutual support. However, there is a high possibility that multiple contexts conflict, which can lead to a problem of context inconsistency. Various methods that include quality of context (QoC) parameters have been employed to solve the issue of context inconsistency. One of the limitations of these methodologies is their inadequate consideration of the context correctness. Moreover, when attempting to address context inconsistency through the utilization of QoC parameters, the conventional Dempster-Shafer (D-S) evidence theory may result in significant conflicts. To tackle the aforementioned concerns, a comprehensive measure of correctness (CMoC) is introduced to offer a more efficient assessment of the correctness of contexts. In light of the limited flexibility in assigning probabilities to contexts using D-S evidence theory, the two-dimensional mass function (TDMF) is introduced. This method allows for assigning probabilities to contexts from various sources. On this basis, a context inconsistency elimination algorithm based on CMoC and TDMF is proposed. The effectiveness of the proposed algorithm has been verified through experimental analyses conducted across various dimensions. Based on the experimental findings, it has been demonstrated that the proposed algorithm's context-judge rate can achieve 97.08%, provided that the high precision configuration of sensors, as outlined in this paper, is utilized. This achievement surpasses that of other inconsistency elimination algorithms by at least 2.05%. [ABSTRACT FROM AUTHOR]

• A novel lattice-based traceable ring signature scheme is proposed. • The proposed scheme is constructed based on a novel TripleRing structure. • A quantum resistant blockchain model based on the proposed scheme is developed. • The experiment is conducted to prove the effectiveness of our scheme. Traceable ring signatures (TRSs) were proposed by Fujisaki and Suzuki (PKC 2007), which is a variant of ring signatures that aims to restrict excessive anonymity of them. If the signer signs two identical messages about the same group of ring members and issue , the two signatures will be detected, but the identity of the signer will not be disclosed; if the signer signs two different messages about the same group of ring members and issue , it allows to reveal the identity of the signer. This primitive is very useful in scenarios that require anonymous protection and avoid duplicate signatures, such as electronic voting, blockchain and Internet of Things. However, the existing post-quantum TRS schemes all have a long signature and low computational efficiency, which is far from practical application. In this paper, we propose an efficient lattice-based TRS scheme by improving DualRing that is a short ring signature scheme proposed by Yuen et al. (CRYPTO 2021). Among all existing post-quantum TRS schemes, the proposed scheme has the shortest signature for a ring size between 5 and 2000, and it is also the fastest one in signature generation, taking only about 0.4 s for a ring size of 1024. In the random oracle model, the proposed scheme is proved to satisfy anonymity, tag-linkability and exculpability under the Module Short Integer Solution and Module Learning With Errors hardness assumptions over lattice. In addition, to demonstrate the applicability of the proposed scheme, we develop a post-quantum blockchain model with access control based on it, which can be extended to Internet of Medical Things (IoMT), e-commerce, education, banking and other systems. [ABSTRACT FROM AUTHOR]

• Using chaotic systems to realize non-interactive integrity audit. • Building integrity audit trees to reduce the computational overhead of entities. • Using the automatic trigger function of smart contracts to design reward and punishment rules. • In order to protect privacy, neither cloud storage nor auditors have access to the raw data. With the widespread adoption of Internet of Things (IoT) technology, a large amount of private data has been generated. Due to the limited computing and storage resources of IoT devices, enterprises and individuals choose to use a cloud storage provider (CSP) to manage their data. However, they lose the actual control of the data, and hence users need to verify the integrity of the data in the cloud. As a result, reducing the computational overhead of integrity auditing tasks for massive amounts of data and optimizing the distribution of benefits among the participants have become the key to the healthy development of IoT technologies. This paper proposes an integrity auditing method based on smart contracts. First, we construct a noninteractive auditing model based on a chaotic system so that the third-party auditor (TPA) can complete the auditing task without challenging the CSP. Second, we proposed a new construction method for audit trees that can quickly generate the verification tag of the root node. Finally, we design a smart contract-based auditing algorithm on the blockchain, which can simultaneously verify the behaviour of the CSP and the TPA to achieve fair payments. The theoretical analysis shows that our scheme can effectively prevent the TPA from falsifying audit results and prevent collusion between the TPA and the CSP. The experimental results show that our scheme has higher audit efficiency. [ABSTRACT FROM AUTHOR]

Due to the limited resources of Internet of Things (IoT) devices, the multimedia application data generated by IoT devices can be offloaded to edge servers for processing. In this paper, we study the credibility-guaranteed multimedia application data offloading problem in MEC systems. Each IoT device competes for the limited transmission resources while ensuring that the credibility requirement is satisfied. We formulate the credible offloading problem with the goal of maximizing Quality of Experience (QoE) for resource-constrained devices. Each IoT device focuses on maximizing its own QoE. The constraints include credibility constraint and resource constraint. We reformulate the problem as a credibility-guaranteed multimedia data offloading game (CMDO-Game). It is proved that for any IoT device, there is an upper bound of communication interference. Based on this property, we further theoretically prove that there is at least one Nash equilibrium offloading strategy in the CMDO-Game. We propose a game-based credible data offloading (GCDO) method to obtain the Nash equilibrium offloading strategy in a distributed way. Finally, the proposed GCDO method is compared with various approximation methods, and a series of experimental results are given. • We study the credibility-guaranteed multimedia data offloading model. • We prove that the proposed game is a potential game, and give the corresponding potential function. • We propose the game-based credible data offloading algorithm to obtain the Nash equilibrium offloading strategy. • We conduct extensive experiments to evaluate our algorithm, and the results verify the superiority of our algorithm. [ABSTRACT FROM AUTHOR]