With the commercialization of private data, location privacy trading in Mobile Crowd Sensing (MCS) has become a fascinating research topic. In consideration of location-dependent sensing tasks, mobile workers take risks at location privacy disclosure when reporting their actual locations. Existing work fail to take workers' diverse privacy protection and trading into account. This paper proposes a novel trading framework with personalized differential privacy guarantee, referred to asLeaper, to bridge the gap between location privacy protection and task allocation efficiency. In particular,Leaperoutputs a personalized obfuscated range for each worker and further obfuscates his location based on a perturbation set within this range by incorporating differential privacy and$k$-anonymity techniques, and thus improves the efficiency of task allocation. Moreover,Leaperquantifies each worker's location privacy loss and compensates him with reasonable payment by running auction in a cost-effective way. Through real-world datasets, our evaluations and analysis demonstrate thatLeaperindeed guarantees all desired properties of personalized differential privacy, truthfulness, individual rationality and budget feasibility.

Mobile crowdsensing (MCS) has emerged as a promising paradigm for collecting high-quality data to support applications, such as large artificial intelligence models and autonomous driving. However, MCS faces two critical challenges: privacy leakage and inefficient task allocation, which hinder its deployment in industrial environments. In this article, we propose a distributed Privacy-preserving and Truthful intelligent Task Allocation (PTTA) system that bridges the gap between theory and practice by addressing data privacy concerns and task allocation inefficiencies. The system is validated through industrial case studies, ensuring its real-world applicability. The main contributions are: 1) a distributed blockchain-based privacy-preserving approach ensuring both data and location privacy, suitable for industrial-scale MCS; 2) a reputation-driven deep-reinforcement-learning-based task allocation algorithm that assigns tasks to trustworthy workers with shorter travel distances, reducing costs and improving data quality; and 3) an effective method for truth discovery and reputation evaluation under privacy constraints, improving the estimation of estimated truth value and workers' reputations. Experimental results show that PTTA improves task allocation efficiency by up to 23.7%–56.5% compared to traditional greedy and ant colony algorithms and the highest accuracy with 4.9%–5.9% increase. This work offers a scalable solution for privacy-preserving MCS in industrial environments.

Multiaccess edge computing (MEC) enables low-latency service delivery by deploying application instances on edge servers. However, edge servers are prone to failures, making it challenging to meet diverse user reliability requirements. A common approach is to deploy redundant instances across multiple edge servers, which improves reliability but increases costs and limits the number of users that can be served within budget. Therefore, efficient deployment strategies are needed to balance cost-effectiveness and reliability guarantees, thereby maximizing the app vendor’s revenue. In this article, we investigate the problem ofRevenue maximization forReliability-awareEdgeApplicationDeployment ($\text{R}^{2}\text{EAD}$). Our objective is to maximize the app vendor’s revenue by deploying its applications on heterogeneous edge servers, subject to budget and resource constraints and users’ diverse reliability requirements. We prove that the$\text{R}^{2}\text{EAD}$problem is$\mathcal {\text{NP}}$-hard and propose an efficient approximation algorithm named$\text{R}^{2}\text{EAD}$-A. By reducing the problem to a nonmonotone submodular maximization problem with curvature$\alpha$under multiple knapsack constraints, we prove that$\text{R}^{2}\text{EAD}$-A achieves a constant approximation ratio of$\frac{1}{\alpha }(1 - e^{-\alpha })$. Extensive evaluations demonstrate that$\text{R}^{2}\text{EAD}$-A outperforms the representative approaches across all tested cases.

User trajectory publication has promoted various location-based applications like user travel recommendation. However, possible privacy leakages have hindered more inclusive trajectory data analysis and utilization. Privacy-preserving trajectory synthesis is a popular approach to address the above privacy issues. Existing methods unavoidably produce low trajectory utility since they usually apply perturbed versions of human moving patterns. Worse still, they cannot adaptively adjust this synthesis according to the varying granularity demands of different users. This paper proposes a novel adaptive trajectory synthesis framework with generated data, namelyADGTrace. Our model achieves privacy preservation without introducing additional noise while maintaining high adaptation.ADGTracedirectly synthesizes artificial trajectories that share the similar patterns with real ones through agenerative and selectiveoptimization process. Additionally, we present a grid granularity alignment strategy to achieve adaptive trajectory synthesis, satisfying varying user demands. Extensive experiments on real-world datasets demonstrate the superiority ofADGTraceover the state-of-the art methods under various utility metrics, maintaining strong attack resilience.

Edge caching is a critical application scenario in edge networks. By storing diverse files on edge servers and dynamically fetching new files from the cloud, edge networks can provide low-latency file access services for mobile users. In practice, the file fetching latency is non-negligible. Consecutive requests for the same missing file during the fetching phase introduce additional latency (referred to as delayed hits). Existing studies either ignore the delayed hits when making caching decisions or are not applicable to multi-server edge networks. In this paper, we investigate the online caching problem with delayed hits in the multi-server edge networks and prove its hardness. The objective is to minimize the total file access latency. To solve the proposed problem, we propose Cadle, which makes caching decisions based on the latency of different file access operations and weights of files in an online manner, without relying on any prior knowledge of future requests. We prove the competitive ratio of Cadle.We also conduct extensive experiments on the real-world dataset to verify the performance of Cadle. The experimental results show that Cadle reduces the total file access latency by at least 31.8% on average, and improves the hit ratio by at least 25.9% on average compared with state-of-the-art approaches.

Although radio frequency (RF)-based activity recognition has made significant progress in recent years, the sensing performance will be significantly degraded under class imbalance conditions, especially when minority and majority classes share semantically similar local motion patterns. Traditional data augmentation approaches in the original sample space may cause semantic deviation and meanwhile bring high computational cost. Instead, in this work we turn to address the issue at feature level, in which we focus on how to distinguish highly similar actions and mitigate imbalance-induced decision boundary bias. To tackle these challenges, we present attention-guided context enhancement network (AceNet), which designs a discriminative feature extractor and develops a feature-augmentation based classifier refinement strategy. Specifically, an attention-guided mechanism is presented to dynamically select the most distinctive temporal segments, and a hierarchical Transformer structure is then proposed to characterize both inner-segment micro-dynamics and inter-segment contextual relationships. Moreover, AceNet synthesizes features via Synthetic Minority Over-sampling Technique (SMOTE) to balance feature distribution for each category and then refine the classifier parameters to mitigate class imbalance bias. Comprehensive experiments on two public datasets with different RF modalities demonstrate that AceNet significantly outperforms existing approaches under various levels of data scarcity at a low cost.

Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge, but its performance can be severely hindered by unreliable wireless transmission and inherent data heterogeneity among clients. Existing solutions primarily address these challenges by incorporating wireless resource optimization strategies, often focusing on uplink resource allocation across clients under the assumption of homogeneous client-server network standards. However, these approaches overlooked the fact that mobile clients may connect to the server via diverse network standards (e.g., 4G, 5G, Wi-Fi) with customized configurations, limiting the flexibility of server-side modifications and restricting applicability in real-world commercial networks. This paper presents a novel theoretical analysis about how transmission failures in unreliable networks distort the effective label distributions of local samples, causing deviations from the global data distribution and introducing convergence bias in FL. Our analysis reveals that a carefully designed client selection strategy can mitigate biases induced by network unreliability and data heterogeneity. Motivated by this insight, we propose FedCote, a client selection approach that optimizes client selection probabilities without relying on wireless resource scheduling. Experimental results demonstrate the robustness of FedCote in DNN-based classification tasks under unreliable networks with frequent transmission failures.

We propose Meta-DPMAPPO /metə,dipi'mæpəʊ/, a a metaverse multi-user service migration framework that combines Multi-Agent Proximal Policy Optimization (MAPPO) with Differential Privacy (DP)-enabled dual-domain perturbation. To maintain usability, we incorporate trajectory topology constraints that balance privacy strength with data availability. The framework enables dynamic service migration, i.e., transferring services to follow mobile users, to ensure low-latency access while safeguarding sensitive user data. We design a migration strategy with multiple migration actions (i.e.,reuse,follow, andnomigration) to minimize global delay and improve resource utilization. We conduct a series of experiments using a combination of public, collected, and synthetic datasets. The results demonstrate that our approach significantly reduces global migration delay in multi-user environments while ensuring privacy protection, and adapts well to different metaverse application scenarios.

Satellite edge computing (SEC) extends the capabilities of task offloading from the ground to near-Earth space by leveraging the wide coverage of satellites. However, the high-speed movement and long-range communication of satellites lead to Doppler shifts and signal loss, which can result in a high bit error rate (BER) and degrade communication link quality. As a result of the high BER, error correction is required, inevitably incurring additional delay during task offloading. Therefore, in this paper, we focus on the problem ofErrorCorrection awareDependencyTaskOffloading (EC-DTO) in satellite environments. By jointly considering task dependencies and BER-induced error correction delays, we formulate the EC-DTO problem as a constrained optimization problem with the objective of minimizing the makespan of users' tasks. To solve this problem, we propose APOS, which is an event-driven offloading framework based on task-AdaptivePrioritization andOptimistic-finish-time-based nodeSelection. Specifically, APOS adopts an online offloading framework that triggers scheduling upon task arrivals and completions with task dependency awareness. It iteratively updates task readiness and performs offloading decisions to reduce the overall makespan in the SEC environment. Simulation results show that APOS outperforms the representative methods, lowering the average makespan by 20.7% and the deadline violation ratio by 84.5% on average across all cases.

In recent years, large language models (LLMs) have shown great potential in many fields. LLMs deployed in cloud data centers are increasingly unable to meet the low-latency inference requirements of massive mobile users. Benefiting from various LLM lightweighting techniques and the continuously improving performance of edge servers, deploying LLMs on edge servers closer to mobile users and executing inference tasks locally can effectively reduce inference latency. However, edge servers have limited storage capacity, and deploying LLMs on edge servers incurs additional deployment overhead. In this paper, we propose an efficient LLM edge collaboration deployment strategy called EdgeColl, aiming to jointly optimize inference latency and LLM deployment costs. Specifically, EdgeColl adopts Low-Rank Adaptation (LoRA) to divide each LLM into a base model and a LoRA matrix. We formulate the LLM edge collaboration deployment problem with LoRA. Then, we present the base model deployment (BMD) strategy to achieve low inference latency and deployment costs. The LoRA deployment (LMD) strategy is also proposed to enable personalized inference. We evaluate the performance of EdgeColl. The experimental results show that EdgeColl effectively reduces LLM inference latency and deployment costs.

The edge computing paradigm provides fine-grained and distributed resources to users with low service latency. To further utilize the advantage of edge computing to improve users' satisfaction, it is essential to jointly optimize service deployment, task offloading, and resource allocation. However, this is challenging because of limited edge resources, diverse task demands, and coupled decisions. In this paper, we propose EdgePro, a novel adaptive edge service provision approach based on safe deep reinforcement learning, aiming to maximize user satisfaction while fulfilling multiple constraints including deployment budget and edge server resources. Specifically, we formulate the optimization problem as a constrained Markov decision process. By designing a constraint-penalty function, we transform the original multi-constraint problem into an equivalent single-constraint problem, addressing the training oscillations caused by conflicts in satisfying multiple constraints. To handle the discrete-continuous coupled decisions, we employ multiple deep neural networks for coordinated control. Then, we propose a safe deep reinforcement learning algorithm based on augmented proximal policy optimization, which adaptively solves the formulated problem while satisfying safety constraints. Experimental results show that EdgePro significantly outperforms benchmark approaches in user satisfaction, convergence speed, and satisfying constraints.

Centralized training for Human Activity Recognition (HAR) typically relies heavily on vast amounts of aggregated data, compromising user privacy. Federated learning (FL) for HAR offers a solution to protect local data privacy. However, existing FL methodologies often fail to fully capture the heterogeneity of user data and the latent correlations among user models, resulting in suboptimal performance and limited robustness. This paper proposes a Correlation-Aware Multi-Similarty Learning Method for Federated HAR, namely MultiSim. Our approach enhances model accuracy with an effective inter-user knowledge learning while protecting data privacy. MultiSim first constructs multiple similarity metrics, and then makes model feature fusion cunningly by the above metrics to learn inherent user similarity profiles. Additionally, we introduce a novel clustering-based FL framework by isolating malicious nodes, thereby mitigating the impact of adversarial attacks. Extensive evaluations on two realworld HAR datasets demonstrate the superiority of MultiSim over other state-of-the-art FL methods under accuracy and robustness. These findings demonstrate MultiSim's potential as a robust and effective solution for HAR.

With the development of blockchain technology, Blockchain-based Crowdsensed Data Trading (BCDT) has emerged as an attractive data exchange paradigm. Although it addresses security issues in data transactions, most recent research primarily focuses on offline scenarios, overlooking the critical importance of enabling real-time online data trading, where it suffers from dynamic worker participation and potential malicious attacks. In this paper, we propose a Blockchain-based Secure and Fair Online Incentive Mechanism (BSFOIM), which primarily incorporates a smart contract called BSFOIMToken, designed to function in online scenarios. In particular, we first introduce a multi-stage auction combined with a time discount factor in BSFOIM to quantify the contribution of workers in completing sensing tasks. Meanwhile, to ensure sensing data quality and worker selection fairness, we propose a Fairness-based Truth Discovery Mechanism (FTDM) with two core modules: a fine-grained reputation system to identify reliable workers and filter out malicious ones, and an upper confidence bound algorithm to optimize worker selection and avoid local optima. Finally, we implement these functions in BSFOIMToken and deploy a prototype on the Ethereum blockchain, demonstrating its practicality and robust performance. Rigorous theoretical and comprehensive experimental tests have proven their adherence to truthfulness, budget feasibility and individual rationality.

As the demand for global computing coverage continues to surge, satellite edge computing emerges as a pivotal technology for the next generation of networks. Unlike ground-based edge computing, Low Earth Orbit (LEO) satellites face distinctive challenges, including high-speed mobility and resource limitations, etc. Therefore, effectively utilizing LEO satellites for global coverage services is crucial but challenging due to their dynamic coverage areas and diverse task requirements. To address these challenges, we introduce a novel dual-cloud edge collaborative task offloading architecture in the satellite network-assisted edge computing environment, namely,Satellite-GroundTaskOffloading (SGTO). The architecture employs a Geostationary Earth Orbit (GEO) satellite and a ground cloud computing center as satellite cloud and ground cloud, respectively, and LEO satellites as edge nodes. We formally define the task offloading problem in theSGTOwith the aim of minimizing the average latency and average energy consumption. We then propose an adaptive approach namedSGTO-Afrom the perspective of satellites to adaptively solve the problem leveraging deep reinforcement learning. Specifically, we transform the task offloading problem into a Markov decision process and adopt the generalized proximal policy optimization (GePPO) algorithm to solve the problem. Finally, experimental results demonstrate thatSGTOarchitecture andSGTO-Aoutperform the representative approaches in terms of average latency, average energy consumption and running time.

In edge computing, a user prefers offloading his/her task to nearby edge servers to maximize the offloading utility. However, this inevitably exposes the user's location privacy information when suffering from the side-channel attacks based on offloading decision behaviors and Received Signal Strength Indicators (RSSI). Existing works only consider the scenario with one untrusted edge server or defend only against one of the attacks. In this paper, we first study the edge task offloading problem with comprehensive privacy protection against these side-channel attacks from multiple edge servers. To address this problem while ensuring satisfactory offloading utility, we develop aLocationImitation-based EdgeTaskOffloading approachLITO. Specifically, we first determine a suitable perturbation region centered at the user's real location for a balance between offloading utility and privacy protection, and then propose a modified Laplace mechanism to generate a fake location meeting geo-indistinguishability within the region. Subsequently, to mislead the side-channel attacks to the fake location, we design an approximate algorithm and a transmit power control strategy to imitate the offloading decisions and RSSIs at the fake location, respectively. Theoretical analysis and experimental evaluations demonstrate the performance ofLITOin improving privacy protection and guaranteeing offloading utility.

Accurate geographical information of nodes is crucial for network applications. However, many existing positioning algorithms face challenges in achieving efficient, accurate, and robust performance when applied to irregular networks with holes or obstacles. Therefore, we introduce a new algorithm, named Network Division Positioning (NDP), to tackle this issue. In NDP, we use a similarity function to derive the distance between neighboring nodes and explore routing paths concurrently, facilitating efficient distance measurement. Next, we analyze measurement errors between landmark nodes to define a threshold that filters out incorrect distances, ensuring measuring and positioning accuracy. To enhance robustness, we first identify collinearity issues by examining the positional relationship between unpositioned nodes and their nearest landmark. Subsequently, we addressed the poor positioning results and built the subnetwork utilizing the nearest landmark node and its associated measurement distance, seeking the most accurate and robust estimated position within this subnetwork. The simulation results demonstrate that NDP outperforms state-of-the-art algorithms in terms of efficiency, accuracy, and robustness when dealing with various irregular networks. Specifically, NDP enhances positioning accuracy by at least 40.82% in terms of the median.

Edge computing enables low-latency services by deploying application instances near users. Application vendors tend to serve more users with higher service satisfaction and a limited budget. This raises a critical yet open problem - optimally deploying application instances and allocating users to edge servers to maximize service utility while fulfilling multiple constraints, including user latency requirements, budget limitations, and edge resource constraints. To address this problem, we formulate it as a constrained optimization problem that jointly solves two tightly coupled sub-problems:ApplicationDeployment andUserAllocation (named as ADUA problem). The objective is to maximize overall service utility by optimizing resource utilization and service satisfaction under latency, budget, and resource constraints. We reformulate the ADUA problem as a multi-armed bandit (MAB) problem and then propose a novel approach namedUMESPbased on penalized MAB framework. Specifically, we design a marginal utility–aware reward function to align bandit learning with the optimization objective. We introduce a penalty mechanism to effectively handle constraint violations. By further integrating the upper confidence bound policy,UMESPachieves adaptive and constraint-aware balance between exploration and exploitation. Theoretical analysis demonstrates thatUMESPachieves bounded regret. Extensive experiments verify thatUMESPexhibits stable convergence in all tests and, on average, outperforms six representative approaches in overall service utility.

Node positioning is an essential function of wireless networks and serves as the foundation for many applications. In the existing works, the cooperative positioning approaches have been extensively studied and are shown to be effective for scenarios with energy and cost constraints. However, these approaches may not perform well in concave networks with holes or obstacles. To address this issue, this paper proposes adistributed cooperative positioning model with adaptive hop-range selection(DCP-AHS for short) for concave networks. DCP-AHS first uses a low-complexity and fast convergent distance estimation method based on the local neighbor nodes. It then uses an adaptive hop-range selection method based on the residual analysis between pairs of anchors. Within the hop range, an unknown node uses multi-lateration with the optimal weight function to determine its estimated position. Finally, a weighted Bounding-Box method with the virtual anchor is employed to avoid significant position estimation errors caused by the collinearity issues. Simulation results demonstrated that the proposed DCP-AHS significantly outperformed the existing algorithms regarding efficiency, accuracy, and stability in various concave networks. Specifically, our proposed model achieved a median improvement of 16.62% to 81.65% in positioning accuracy compared to the comparison algorithms.

Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This naturally poses a challenge to find more suitable computation offloading strategies with minimum network latency and energy consumption, especially when a large number of co-existing users are to offload their tasks. In this paper, therefore, we mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering LEO satellites' mobility and SECN's heterogeneous resource constraints to explore more practical computation offloading strategies. We first formulate the problem ofMobility-awareComputationOffloading (MCO) in the SECN via specifying the effect of LEO satellites' high-speed movement on the computation offloading, aiming to minimize the network latency and energy consumption. Considering the MCO problem is discrete and non-convex as the objective function and constraints are associated with the binary decision variables. We then convert the original non-convex problem into a continuous convex problem which is proved to be feasible. To avoid a high computational complexity incurred by the extensive co-existing user offloading, we designMCO-A, a distributed algorithm based on ADMM (alternating direction method of multipliers) to solve the MCO problem efficiently. Finally, the performance ofMCO-Ais evaluated via extensive experiments including small-scale and large-scale scenarios. The experimental results show that MCO-A can achieve a lower network latency and energy consumption in an efficient way compared with the baseline and state-of-the-art approaches.

Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novelAvailability-awareRevenue-effectiveApplicationDeployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is$\mathcal {NP}$-hard. Then, we propose an approximation algorithm namedARAD-Ato find the ARAD solution efficiently with a constant approximation ratio of$\frac{1}{2}$. We extensively evaluate the performance ofARAD-Aagainst five representative approaches. Experimental results demonstrate that ourARAD-Acan achieve the best performance in securing the app vendor's overall revenue.

Edge computing (EC), as an emerging technology, allows app vendors to deliver low-latency services by allocating users to edge servers nearby. Unfortunately, these heterogeneous and resource-constrained edge servers struggle to serve all the co-existing users with ever-growing service requirements, especially during peak periods. Existing approaches for the user allocation problem insisting on fulfilling prescribed service requirements often fail to tackle this issue and consequently result in a tremendous user loss that significantly impairs app vendors' service profit. We observe that a certain degree of violation of users' requirements can help spare resources to serve more users. Meanwhile, such a violation, which is also called service degradation, can be tolerated by users if they receive appropriate compensation. However, abusive degradation may introduce huge compensation, ultimately leading to reduced service profit. As a result, the service degradation and compensation must be carefully considered by app vendors. In this paper, we study theServiceDegradation-ToleratedOnlineUserAllocation (SD-OUA) problem in EC environment, aiming to maximize app vendors' service profit. We prove the$\mathcal {NP}$-hardness of the SD-OUA problem. Then, we propose theDegradation-awareUserAllocation (DUA) approach based on problem reformulation and primal-dual optimization to find its solutions in polynomial time. The performance ofDUAis theoretically guaranteed and evaluated against three representative approaches via extensive experiments.

Edge computing (EC) provides low-latency services by deploying edge servers close to users. However, these servers are prone to failures that can invalidate any predefined user allocation strategies. To ensure continuous services and maintain users' payments, affected users who are disconnected from the failed edge servers need to be reallocated. Unfortunately, due to the strict latency requirements of users and the limited resources on edge servers, many of them fail to be reallocated. Thus, we propose to migrate unaffected users from affected users' nearby edge servers to free up more resources for reallocation. In this paper, with the aim of maximizing the overall revenue and ensuring continuous service provisioning for users, we formulate the problem of Migration-Assisted _User _Reallocation (MUR) upon edge server failures and prove its NP-hardness. We then introduce an Integer Programming-based approach named MUR-O to find the optimal solution and a heuristic approach named MUR-H to efficiently find sub-optimal solutions. Experimental results on real-world datasets demonstrate that our approaches are superior to three representative approaches.

As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive role in congestion control, resource allocation, and anomaly detection. The trend of network traffic is different at different scales, so multiscale is an important characteristic of network traffic. In addition, the network traffic is nonlinear on each scale and dependent between scales. The existing NTP methods cannot comprehensively consider these characteristics, which limits their performance. In view of the characteristics of network traffic, such as multiscale, nonlinearity, and scale dependence, this article proposes a new multiscale NTP method based on a deep echo-state network (ESN). First, a multiscale parallel layered structure based on deep ESN is designed to fully consider the influence of each scale on the prediction result and then reduce the prediction error. Second, a feature extraction algorithm is proposed to improve the nonlinear approximation ability by extracting more abundant dynamic features with multiple reservoirs. Third, an NTP model based on scale dependence is proposed to reduce the influence from partial scale missing and then improve the prediction accuracy. Finally, simulation results demonstrate that compared with the state-of-the-art NTP methods, the proposed method significantly improves the prediction performance of network traffic with a slight increase in running time.

Satellite Internet of Things (S-IoT), which integrates satellite networks with IoT, is a new mobile Internet to provide services for social networks. However, affected by the dynamic changes of topology structure and node status, the efficient and secure forwarding of data packets in S-IoT is challenging. In view of the abovementioned problem, this paper proposes an adaptive routing strategy based on improved double Q-learning for S-IoT. First, the whole S-IoT is regarded as a reinforcement learning environment, and satellite nodes and ground nodes in S-IoT are both regarded as intelligent agents. Each node in the S-IoT maintains two Q tables, which are used for selecting the forwarding node and for evaluating the forwarding value, respectively. In addition, the next hop node of data packets is determined depending on the mixed Q value. Second, in order to optimize the Q value, this paper makes improvements on the mixed Q value, the reward value, and the discount factor, respectively, based on the congestion degree, the hop count, and the node status. Finally, we perform extensive simulations to evaluate the performance of this adaptive routing strategy in terms of delivery rate, average delay, and overhead ratio. Evaluation results demonstrate that the proposed strategy can achieve more efficient and secure routing in the highly dynamic environment compared with the state-of-the-art strategies.

Abstract The ever‐evolving trip mode of human being leads the automobiles moving toward connected, autonomous, sharing, and electrified vehicles rapidly. But the connection introduces new cybersecurity problems on in‐vehicle networks, which poses great challenges for safety guarantee of distributed automotive cyber‐physical systems. This article first analyzes the cybersecurity vulnerabilities and defines the security requirements for in‐vehicle networks, and then introduces the architecture evolution of in‐vehicle network. Based on the definition on architecture of in‐vehicle networks, this article defines a security protection framework for it. And then, it surveys the state‐of‐the‐art works for availability protection, integrity protection, and confidentiality protection of in‐vehicle networks, respectively, and detailed analysis and comparisons are given about the proposed cybersecurity protection mechanisms. Finally, it summarizes the future challenges for cybersecurity protection of in‐vehicle networks, and proposes possible solutions for these challenges.

Abstract Network traffic prediction is of great significance to resource management in cyber‐physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short‐term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self‐similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN‐AR). First, the framework of ESN‐AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN‐AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN‐AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state‐of‐the‐art models, the proposed method achieves more accurate and stable prediction performance.

The deployment of Mobile Edge Computing (MEC) servers on Low Earth Orbit (LEO) satellites to form MEC satellites is of increasing concern. A routing strategy is the key technology in MEC satellites. To solve the uncertainty problem of LEO satellite link information caused by complex space environments, a routing strategy for LEO satellite networks based on membership degree functions is proposed. First, a routing model based on uncertain link information is established. In particular, the membership function is designed to describe the uncertain link information. Based on this, the comprehensive evaluation of the path is calculated, and the routing model considering uncertainty is established with the comprehensive evaluation of the path as the optimization objective. Second, in order to quickly calculate the path, a grey wolf optimization algorithm is designed to solve the routing model. Finally, simulation results show that the proposed strategy can achieve efficient and secure routing in complex space environments and improve the overall performance compared with the performances of traditional routing strategies.

As an important issue in the trajectory mining task, the trajectory clustering technique has attracted lots of the attention in the field of data mining. Trajectory clustering technique identifies the similar trajectories (or trajectory segments) and classifies them into the several clusters which can reveal the potential movement behaviors of nodes. At present, most of the existing trajectory clustering methods focus on some spatial properties of trajectories (such as geographic locations, movement directions), while the spatial-temporal properties (especially the combination of spatial distances and semantic distances) are ignored, and thus some vital information regarding the movement behaviors of nodes is probably lost in the trajectory clustering results. In this paper, we propose a Joint Spatial-Temporal Trajectory Clustering Method (JSTTCM), where some spatialtemporal properties of the trajectories are exploited to cluster the trajectory segments. Finally, the number of clusters and the silhouette coefficient are observed through simulations, and the results show that JSTTCM can cluster the trajectory segments appropriately.

The Underwater Sensor Network (USN) has great advantages in marine environmental monitoring. When collecting the perception information of sensor nodes, the mobile node can effectively compensate for the shortcomings of traditional multi-hop transmission modes. However, the complex marine environment causes the location uncertainty of nodes. Therefore, a path planning method based on the location uncertainty of water surface nodes in USN is proposed in this paper. Firstly, the structure of the USN based on the mooring model is introduced and the problem model of path planning is proposed. Secondly, the inevitable communication circle is obtained by analyzing the deviation range and communication range of water surface nodes. Thirdly, Convex Hull algorithm is used to plan the path in accordance with the inevitable communication circle of water surface nodes. Finally, simulation results show that the proposed method can obtain a shorter path under the premise of ensuring the completion of information collection.

A distributed image compression scheme based on solar energy harvesting is proposed to address the problem of image transmission in wireless multimedia sensor networks. Two-level clustering management is adopted. The camera node-normal node cluster enables camera nodes to gather and send collected raw images to the corresponding normal nodes for compression, and the normal node cluster enables the normal nodes to send the compressed images to the corresponding cluster head node. The re-clustering and dynamic adjustment methods for normal nodes are proposed to adaptive adjustment the operation mode in the working chain. Simulation results show that the proposed distributed image compression scheme can effectively balance the energy consumption of the network. Compared with the existing image transmission schemes, the proposed scheme can transmit more and higher quality images and ensure the survival of the network.

The hop‐based localisation algorithm uses hop‐by‐hop propagation to establish node‐to‐anchor distance estimation, which does not require costly and complicated ranging hardware. This helps boost system performance, while minimising the cost of localising the nodes within the network. However, the application of hop‐based localisation algorithms is restricted due to their dramatic accuracy degradation in irregular network, which is mainly caused by the large error of distance estimation. The authors find that the error variance of the estimated distance increases as the hop count increases, i.e. there is a heteroscedasticity problem in the distance estimation process, which will affect the location estimation. In this study, by exploring the error during the location estimation, they aim to find and employ the optimal weighted function to improve localisation accuracy. A geometric constraint algorithm is also devised to correct the incorrectly estimated location by mitigating the adverse effects from flip ambiguity. By combining the optimal weighted function and the geometric constraint algorithm, a novel hop‐based localisation algorithm is proposed in this study. Both the theoretical analysis and experimental results show that the proposed method has not only maintained the economic characteristics of hop‐based localisation, but also has the high localisation accuracy where it can be adapted to various networks with different node distributions.

Echo state network (ESN), which was proposed as a novel recurrent neural network (RNN), has already been proved to exhibit better prediction ability than traditional neural networks in dealing with time series prediction. However, ESN's randomly generated reservoir structure is of high complexity and can not guarantee the stability of the prediction. In this paper, we propose a novel ESN with deterministic multiple loops reservoir structure (MLR) to avoid the randomness of the reservoir in the classic ESN. In addition, compared with the adjacent-feedback loop reservoir structure (ALR), the novel reservoir structure strengthens the connection of neurons in the reservoir and improves the nonlinear approximation ability of ESN. To test its performance, our MLR-based ESN is applied to network traffic prediction. Extensive simulation results regarding to different prediction steps demonstrate that MLR can achieve higher prediction accuracy, and outperform existing prediction models. Furthermore, we also analyze the influence of parameters of MLR on the prediction accuracy, such as the neuronal interval of multiple loops and the number of loops.

How to improve the design efficiency of complex product with meta-synthesis is a valuable research problem. The research achievements of complex product design based on metasynthesis, which has been made by the author's research group, is summarizd in this paper. First, the process model and the general framework of complex product design based on metasynthesis are established. Then, the architecture and the key technologies of HWME is presented, including group argumentation and group decision, project management, 3-d visualization, model and data management. Finally, a prototype system of HWME for complex product design is developed.