Federated Learning (FL) has become a prevalent distributed paradigm for privacy-sensitive edge applications, thanks to its decentralized data storage without raw data transmission. However, the distributed nature of FL brings critical model security challenges, especially for intellectual property ownership protection and malicious accountability tracing. Existing researches rarely propose a holistic solution that balances privacy preservation, model robustness and training efficiency concurrently. To address these limitations, this paper presents a novel framework named Secure and Traceable Federated Learning for Edge Computing (STFL-EC). Specifically, a K-medoids clustering based delay-aware client grouping method is designed to alleviate the straggler effect and improve training efficiency. Besides, a periodic watermark embedding scheme is developed to assign unique watermarks for different training cycles, enabling reliable ownership verification and tampering resistance. Moreover, an enhanced watermarking strategy combining traitor tracing and Enhanced Watermark Embedding (EWE) is proposed to ensure accountability with negligible model performance loss. Extensive experiments verify that STFL-EC reaches 98.3% watermark detection accuracy, induces less than 1% model performance degradation, and cuts total training latency by around 21%, offering a practical secure FL solution for resource-constrained edge environments.

To meet the sustainable connectivity requirements of 6G terrestrial networks, energy self-sufficiency and large-scale scalability have emerged as critical challenges. The Simultaneous Wireless Information and Power Transfer (SWIPT) technology provides a promising technical route for Battery-free SWIPT-enabled Sensor Networks (BSSN). However, due to the dynamic coupling and uncertainty inherent in spatial-temporal data and energy flows, existing solutions struggle to balance scalability and energy sustainability. To tackle this issue, this paper proposes a Spatial-temporal Fuzzy logic-driven Topological Decoupling mechanism with Balanced Supply and Demand (SFTD-BSD). A Multidimensional Spatial-Temporal Fuzzy prediction (MSTF-prediction) model combining Analytic Hierarchy Process (AHP) and Sparse Bayesian Learning (SBL) is developed to predict data and energy flows and capture spatial-temporal correlations. An Optimized Case-based Reasoning Inference Rule Generation (OCR-IRG) method is designed to enable online learning and adaptive inference, thereby smoothing and suppressing prediction errors. A lightweight fuzzy output scheme is further proposed to achieve robust Master Node (MN) election, thereby realizing dynamic topology decoupling and energy self-sustainability with low computational overhead. Extensive experiments demonstrate that SFTD-BSD significantly outperforms LEACH, LEACH-R, HHCA, and GWOA-CH in energy sustainability and scalability, validating its effectiveness for energy-harvesting BSSN and future 6G terrestrial networks.

Clustering mechanism and the Wireless Energy Transfer (WET) technology are pivotal for enhancing the energy efficiency of the Wireless Sensor Network (WSN). However, existing clustering algorithms often fail to fully optimize energy efficiency, while WET face the challenge of spatial-temporal instability in energy. To address these challenges, an Energy efficient Clustering Approach based on wireless Energy Provision (ECAEP) mechanism is proposed in this paper. Specifically, a Clustering method integrated the Probability Theory and Multiple Factors (CPMF) is presented, in which multiple factors are taken into consideration to optimize cluster head election. Additionally, an energy provision mechanism based on WET is designed to enable mutual energy complementarity among sensor nodes. The issue of energy supply path planning is transformed into a shortest path problem, which is solved through the Dijkstra-based Energy efficient Supply Attenuation Coefficient (DESAC) algorithm. Extensive simulations are conducted to evaluate the effectiveness of ECAEP, and results exhibit improvements in packet delivery to the Sink by 26.5%, 27.0%, and 35.3% over LEACH; by 28.9%, 8%, and 8.5% over R-LEACH, by 12.3%, 14.0%, and 15.1% over HHCA, by 6.3%, 6.1%, and 10.9% over GWOA-CH in three different scenarios respectively.

This paper introduces an advanced near-field multi-user access system that integrates the programmable metasurface, also known as Reconfigurable Intelligent Surfaces (RIS), with Orbital Angular Momentum (OAM) technology to enhance spectral efficiency and reduce interference. The system employs a multi-mode OAM transmitter to generate signals carrying multiple data streams, which are directed toward a metasurface-based RIS. The RIS is designed to receive incoming OAM beams, demultiplex the data, and dynamically focus the signals on specific spatial regions, ensuring high Signal-to-Noise Ratio (SNR) and minimal interference for efficient multi-user transmission. To achieve adaptive beam control, a 2-bit transmissive RIS is utilized, allowing dynamic adjustments of OAM modes and enabling precise energy focusing at various user locations in the near-field. The orthogonality of OAM modes further contributes to increased spectral efficiency. Furthermore, extensive full-wave simulations and a complete communication test environment are developed, covering the entire transmission process from the OAM transmitter to the RIS-assisted communication link. Experimental results demonstrate that multi-mode OAM beams are effectively converted to spot focusing through RIS and achieve the same-frequency data separation at each focal point. This novel approach offers a high-spectral-efficiency, low-interference communication solution. It provides valuable insights into improving multi-user access and data transmission efficiency in various IoT applications, including smart factories, logistics hubs, and in-vehicle communication networks.

With the rapid increase in the number of vehicles, research on license plate recognition (LPR) is becoming increasingly important. However, most of the existing research fails to meet the requirement on fast recognition speed and high recognition accuracy concurrently. In this article, a delay-precision-balanced approach for LPR based on fog (DLPRF) computing is proposed, with the aim of reducing the response latency while ensuring the recognition accuracy. Specifically, a new license plate detection (LPD) framework YOLOv5LPD is proposed, which employs the bidirectional feature pyramid network (BiFPN) as an intermediate layer to effectively integrate features of different scales. By means of removing the detection head for large objects, YOLOv5LPD is able to simplify the network structure and reduce the complexity. Additionally, a lightweight convolutional recurrent neural network (CRNN) is presented for license plate character recognition without the operation of character segmentation. Furthermore, a fog computing-based LPR task offloading algorithm is proposed to significantly reduce the queuing delay in task transmission and execution. Experimental results demonstrate that with dataset Chinese City parking dataset (CCPD), YOLOv5LPD achieves an$mAP0.5:0.95$of 0.786 in LPD, with a speed improvement of 7% and 30% over YOLOv5s and YOLOv4, respectively. In addition, YOLOv5LPD still holds 95.2% accuracy in cross-data LPR experiments, demonstrating the robustness of the model. Meanwhile, the task offloading algorithm in DLPRF also greatly enhances response speed.

With the improvement of edge-based autonomous systems such as mobile Industrial IoT (IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the requirements of real-time video services. However, existing encoding standards for video services in Web 2.0 are specifically designed for something other than IoT video streaming. We have improved our Adaptive Compression-Reconstruction (ACORN) framework to obtain ACORN+, based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. Given that multiple kinds of intelligent tasks are expected to be finished on the device side, we also designed a device-cloud collaboration scheme where deep learning-based algorithms can be executed on both the device and server sides. Experiments reveal that video analytics can be conducted on compressed frames. Taking action recognition as a device-cloud collaboration use case, we find ACORN \(+\) obtains more than 3 \(\times\) speedup on compressed frames. The reconstruction algorithm in ACORN \(+\) is with 1– 4 dB improvements. Moreover, the encoding time cost and the encoded video volume are reduced by more than 4 \(\times\) under the ACORN \(+\) framework. 1

Abandoned object Detection (Aod) is of critical importance in the field of public safety. However, the demand on detection accuracy and latency hinders the development of ubiquitous Aod in safety protection, especially for some surveillance devices with relatively low-computational capacity. To this end, a novel high-precision and low-latency Aod method under the hybrid cloud-fog computing architecture is proposed in this article. To be specific, a YOLO-various hidden (YOLO-VH) Aod network model, which is integrated with an efficient dynamic convolution-based ghost module and a Haar wavelet-based downsampling convolution module, is presented to improve the detection accuracy of Aod. In addition, a flexible task offloading strategy is proposed to offload some of the Aod tasks based on the expectation cursor, which is designed to determine the local optimal offloading amount at different times. Finally, extensive experiments are conducted to verify the performance of our proposal through simulations. Our proposal exhibits a reduction of approximately 5.31 million parameters and 30.4 GFLOPs in computation compared with YOLOv9, while demonstrating performance improvements of 25.0% and 38.8% relative to cloud and fog computing, respectively. Furthermore, the total latency for image acquisition, task offloading, and task processing has been observed to be approximately 60% and 15% lower than cloud and fog computing, respectively.

Wireless Sensor Networks (WSNs), which provide perception services for the Internet of Things (IoT) infrastructure, usually suffer from constrained energy resources. However, the fact that the data collected by WSNs often exhibit spatial-temporal correlation leads to the waste of energy. In addition, load imbalance among sensor nodes also makes energy efficiency low. To this end, a novel Energy-efficient approach based on Clustering using Grey Prediction (ECGP) is proposed in this paper. To be specific, a novel Dual-end Data Prediction Mechanism (DDPM) is presented based on the grey prediction model to cut down data redundancy. Furthermore, the prediction process of the grey model, namely a dynamic and fixed size prediction queue scheme, is optimized to enhance the prediction accuracy. A novel Energy-Distance Factor (EDF) and a novel Dual-Threshold-based Critical Condition (DTCC) are proposed with the aim of realizing load balance and alleviating the challenge resulted from random events occurrence. Finally, extensive experimental simulations have been carried out to demonstrate the energy efficiency of ECGP. It is compared with the classic and several latest clustering algorithms, namely, LEACH, R-LEACH, EAHA and ECPSO. The experimental results indicate that ECGP outperforms the others in terms of the network lifetime, the throughput, and the energy efficiency.

Task offloading (TO) is beneficial to reducing the delay and energy consumption for the prosperity of the applications in next generation (NG) wireless networks. However, existing TO approaches are inability to exhibit low complexity and stable performance. To this end, a novel federated hierarchical deep deterministic policy gradient (FHDDPG) algorithm for TO and resource allocation (RA) is proposed in this article. To be specific, three deep deterministic policy gradient (DDPG) modules are deployed in parallel to make offloading decision on the execution mode of tasks and the proportion allocation of the transmission rate. Subsequently, a federated learning method is proposed to collaboratively train the HDDPG model by means of sharing models’ weights. Meanwhile, the delay and the energy consumption are comprehensively considered as the average system consumption, which is defined as a reward metric of FHDDPG. Finally, extensive simulations are conducted to demonstrate the effectiveness of our proposal. The experimental results indicate that the average system consumption of FHDDPG is cut down by 11.4% and 18% compare with HDDPG and DDPG, respectively, which means FHDDPG can achieve a better performance effectively.

Meta-surfaces, also known as Reconfigurable Intelligent Surfaces (RIS), have emerged as a cost-effective, low power consumption, and flexible solution for enabling multiple applications in Internet of Things (IoT). However, in the context of meta-surface-assisted multi-pair IoT communications, significant interference issues often arise amount multiple channels. This issue is particularly pronounced in scenarios characterized by Line-of-Sight (LoS) conditions, where the channels exhibit low rank due to the significant correlation in propagation paths. These challenges pose a considerable threat to the quality of communication when multiplexing data streams. In this paper, we introduce a meta-surface-aided communication scheme for multi-pair interactions in IoT environments. Inspired by holographic technology, a novel compensation method on the whole meta-surface has been proposed, which allows for independent multi-pair direct data streams transmission with low interference. To further reduce correlation under LoS channel conditions, we propose a vortex beam-based solution that leverages the low correlation property between distinct topological modes. We use different vortex beams to carry distinct data streams, thereby enabling distinct receivers to capture their intended signal with low interference, aided by holographic meta-surfaces. Moreover, a prototype has been performed successfully to demonstrate two-pair multi-node communication scenario operating at 10 GHz with QPSK/16-QAM modulation. The experiment results demonstrate that, even under LoS conditions, the isolation between the two-pair channels exceeds 21 dB. This allows receiving users to undertake simultaneous, same-frequency multiplexed data transmission under extremely low interference conditions, with a real-time demodulation Bit Error Rate (BER) remaining below 3.8×10-3 at achievable Signal-to-Noise Ratio (SNR) conditions. Through the convergence of holographic meta-surfaces and vortex beams, we present a fresh perspective on achieving efficient, low-interference multi-pair IoT communications.

Combination with the Simultaneous Wireless Information and Power Transfer (SWIPT) technology is expected as a promising solution to the issue of energy constraint in Wireless Sensor Networks (WSN). However, little attention is paid to the energy sustainability in SWIPT-enabled WSN with limited Mobile Energy Access Point (MEAP) configurations. To this end, a novel Energy-sustainable approach for SWIPT-enabled WSN with Constraint MEAP (ESWCM) configurations is proposed in this paper. To be specific, an Optimal Ring Width Determination (ORWD) algorithm is proposed, with the aim to ensure all the sensor nodes lie in the energy coverage of limited MEAPs from the perspective of the entire network. Subsequently, a GA-based Clustering algorithm constrained by cluster Head (GCH) connectivity is presented to promote the survivability of Cluster Heads (CHs) and the effectiveness of data transmission through reasonable cluster size. Additionally, the optimal parameters of each sensor node are determined through an Energy-efficient Parameter Optimization (EPO) algorithm to achieve energy sustainability of each individual sensor node. Experimental results indicate that ESWCM exhibits higher node survival rate by 124.7%, 2%, 29.9%, and 40.5% compared with LEACH, S-LEACH, R-LEACH, and DEEC after 1200 iterations respectively. Moreover, it also shows a survival rate of 100% after adjustments.

The emergence of the Simultaneous Wireless Information and Power Transfer (SWIPT) technology makes it possible to achieve energy sustainability in the Wireless Sensor Networks (WSNs). However, little attention was paid to the large-scale SWIPT-enabled WSNs. To this end, we synthesize the network Energy Efficiency (EE), energy sustainability conditions, and network throughput in the large-scale SWIPT-enabled WSNs, and propose a Topology-Scale-Adaptive and Energy Efficient Clustering Scheme (TSA-EECS). To be specific, the EE maximization problem is formulated as a fractional programming problem, which jointly optimizes the transmission power and the power splitting ratio of sensors. Subsequently, a Dinkelbach-based iterative algorithm is proposed to transform the problem and a Lagrangian function is presented to obtain a near-optimal solution through the gradient descent method. In addition, a BFOA-based Cluster Head (CH) selection algorithm is proposed to adapt to the large-scale network and reduce energy consumption for CH selection. Finally, extensive simulations are conducted to evaluate the effectiveness of TSA-EECS. Experimental results demonstrate that the proposed iterative algorithm converges after 15 iterations on average. In addition, compared with recent energy harvesting schemes, the network throughput of TSA-EECS is improved by 3–8%.

IoT devices are enabled to capture and upload videos with increasing bitrates. Massive IIoT is eager for effective video processing techniques to satisfy the requirements of real-time video services. With the emergence of 5G-unlicensed (5G-U), ultra-low latency video applications become possible. However, existing encoding standards for video services in Web 2.0, such as H.265, are not naturally designed for IIoT video streaming, leading to bandwidth pressure where 5G-U coexists with various other wireless signals. To tackle this problem and to support low-latency video utilization by IIoT video sources, we propose an Adaptive Compression-Reconstruction framework named ACORN, which is based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. We design a QoE-aware parameter selection mechanism to deal with volatile network environments during compression. With learnable gated convolution layers and channel-wise soft-thresholding operators, ACORN also builds a real-time reconstruction module. Experimental results reveal that video analytics can be conducted on compressed frames. The reconstruction algorithm in ACORN is with $1-4 \mathrm{~dB}$ improvements. Moreover, both the encoding time cost and the encoded video volume are reduced by more than $4 \times$ under the ACORN framework.

In the scenario of the Internet of Things (IoT) co-located with the cloud, many applications, such as face recognition, traffic monitoring, and medical diagnosis, usually outsource a large amount of generated image data to cloud servers (CSs) to reduce the burden of local storage. Many secure encrypted image retrieval schemes have been proposed to protect data privacy. However, existing work incurs storage and communication burdens, lacks verifiability of query results and has potential forward security threats. To solve these issues, we propose the VerFHS framework in this article, which can satisfy Verifiability, Feedback, and High-Security. Specifically, we first present an extended secure$k$-NN algorithm to protect indexes, cleverly design ciphertext inner product for similarity comparison, and use the reward mechanism of blockchain to build a monitoring and feedback mechanism for CSs. Then we demonstrate an enhanced VerFHS scheme in the dynamic setting (VerFHSD) that uses a permutation matrix to process image encryption against adaptive attacks during dynamic updates. VerFHSD prevents CSs from making search queries over newly added images via previous tokens, thereby achieving forward security. The formal security analysis shows that our schemes protect the privacy of images, indexes and query tokens, and forward security. And extensive experiments using the real-world data set demonstrate that our scheme not only has the highest search accuracy all the time, but also achieves efficient queries at the millisecond level, when compared with other advanced image retrieval schemes.

Abstract Energy inequality caused by the process of cluster head election has a large influence on energy efficiency and the network lifetime of wireless sensor networks (WSNs). To this end, a novel concept of EI ec is proposed to evaluate the equality degree of energy consumption. Related theorems for establishing the candidate set of cluster heads are proposed, with the aim of promoting energy equality in each cluster. Subsequently, a novel energy‐efficiency‐adaptive cluster formation mechanism based on economic (ECFE) theory is proposed and detailed. Finally, extensive experiments are carried out to assess its energy efficiency and the network performance by comparisons with the existing classic and latest intelligent clustering algorithms. The results indicate that ECFE improves not only the energy efficiency but also the network performance effectively.

Person re-identification (Re-ID) is a challenging and arduous task due to non-overlapping views, complex background, and uncontrollable occlusion in video surveillance. An existing method for capturing pedestrian local region information is to divide person regions into horizontal stripes, which may lead to invalid features and erroneous learning. To solve this problem, this paper proposes a 3D skeleton and a two-stream approach to person Re-ID. The first stream of the method uses the 3D skeleton for background filtering and region segmentation. The second stream uses Siamese net to extract the global descriptor. The features of the two streams are fused to preserve the integrity of the person. An optimized region matching method for metric learning is designed. Extensive comparing experiments were conducted with state-of-the-art Re-ID methods on the Market-1501, CUHK03, and DukeMTMC-reID datasets. Experimental results show that the proposed method outperforms the existing methods in recognition accuracy.

A novel intercluster routing which simultaneously takes the energy efficiency in both intracluster and intercluster phases into account is proposed in this article, with the aim of extending the lifespan of the wireless sensor networks (WSNs). In the intracluster phase, the data are acquired based on the compressive sensing (CS) theory to cut down extra energy consumption resulted from spatial-temporal correlation. As for the intercluster phase, the economic welfare theory is applied to balance the energy depletion among different clusters. To this end, a novel concept of energy efficiency welfare (E2W) is proposed to promote energy equilibrium during the process of intercluster routing decision making. Subsequently, an energy-saving intercluster routing integrated economic theory with CS (EIREC) is presented and detailed. Finally, extensive experiments are designed and conducted to evaluate its energy efficiency. Comparisons with the existing clustering and CS-based strategies have verified its effectiveness in improving energy efficiency and extending network lifespan.

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.

The participant's reputation is vital to improve the quality of service for Mobile Crowd Sensing (MCS). A multidimensional reputation evaluation model was proposed in this paper to evaluate the participant's reputation more objectively. Different from the existing strategies, the service delay and the count of the successful as well as the failed transactions were additionally utilized to evaluate the participant's reputation. An algorithm based on Analytic Hierarchical Process (AHP) was presented to establish the reputation evaluation weight matrix. Besides, a fuzzy logic based mechanism was proposed to normalize the value of the four criteria and a dual-threshold mechanism was designed to achieve admission control more properly. Finally, extensive simulations were conducted and the simulation results confirmed the effectiveness of the reputation evaluation model.