ABSTRACT Objectives In rapidly evolving industrial environments, resource management in Mobile Edge Computing (MEC) has gained increasing attention, aiming to ensure Quality of Service (QoS) for Artificial Intelligence of Things (AIoT) applications. While MEC reduces end‐to‐end delay, tasks offloaded to the cloud still encounter bottlenecks when processing massive AIoT‐generated data streams. To overcome this, we introduce a Collaborative Edge‐Edge (CE2) architecture that integrates heterogeneous edge servers and devices, enabling real‐time latency‐energy trade‐offs and accelerating AI‐driven decision‐making at the network edge. Managing resources in such dynamic, multi‐task, multi‐server environments remains challenging, especially under variable task‐arrival rates. Methods To tackle this, we propose LyDRM, a hybrid dynamic resource management scheme that synergistically combines model‐based optimization with model‐free deep reinforcement learning (DRL). A Lyapunov optimization module is embedded to enforce queue‐stability constraints, ensuring bounded task backlogs over time. Result To validate its effectiveness, extensive simulations show that LyDRM reduces the average weighted system cost‐defined as a combination of latency and energy metrics‐by at least 39.89%, significantly lowers both latency and energy consumption, accelerates convergence, and maintains long‐term stability in dynamic AIoT scenarios.

The edge intelligence-enabled Social Internet of Things (SIoT) faces severe security threats from stealthy malware propagation, while existing defenses struggle to model complex behaviors or provide real-time and privacy-aware responses. Herein, we propose a comprehensive malware defense framework integrating a five-state propagation model, continuous-time differential games, and a privacy-aware reinforcement learning algorithm named PP-D3QN (Privacy-Preserving Dueling Double Deep Q Network). The malware propagation model includes susceptible, infectious, patched, quarantined, and removed states, accurately representing centralized and cooperative patching as well as quarantine detection mechanisms. Leveraging differential games, optimal defense strategies are theoretically derived by solving the Hamilton–Jacobi– Bellman equation, dynamically balancing infection risk, patching benefits, and quarantine costs. The PP-D3QN algorithm employs prioritized experience replay with strict control over private data sampling and Gaussian noise perturbation to ensure differential privacy, while learning effective defense strategies through practical interaction with dynamic edge intelligence-enabled SIoT systems. Extensive simulations demonstrate that the proposed method significantly improves malware suppression speed and SIoT nodes recovery rates, showcasing strong theoretical and practical value. This work offers a rigorous and applicable solution for dynamic malware defense under privacypreserving constraints in edge intelligence-enabled SIoT systems.

With the rapid development of intelligent transportation and vehicular edge computing (VEC), efficient, fair, and interpretable task offloading has become a key challenge in dynamic and resource-constrained environments. Non-orthogonal multiple access (NOMA) can enhance connectivity and spectrum efficiency. However, conventional resource allocation strategies typically rely solely on channel gain ordering while overlooking spatial factors and fairness. In addition, the lack of transparency in multi-agent deep reinforcement learning (MADRL) decision-making raises concerns regarding transparency and trustworthiness. To address these challenges, we propose a NOMA-based task offloading framework that integrates distance and channel-aware resource allocation, and we design a distributed multi-agent decision-making algorithm based on potential games (DACA-MAD4PG), further incorporating Shapley Additive Explanations (SHAP) to improve interpretability. The proposed framework is significantly different from existing NOMA-based task offloading approaches in the following three aspects. First, it introduces a distance and channel-aware joint resource allocation mechanism to enhance both efficiency and fairness in vehicular edge computing. Second, an exact potential game is incorporated to guarantee system stability and the existence of Nash equilibria. Last, SHAP is integrated to provide post hoc interpretability, thereby improving transparency in multi-agent decision-making. Experiments based on real-world DiDi trajectory data demonstrate that the proposed approach significantly reduces task latency, improves service success rate, cumulative reward, and fairness, and outperforms several baselines, thereby providing a stable and interpretable solution for VEC task offloading.

The rapid development of the social Internet of Things (IoT) enhances interconnectivity but also raises significant network security challenges, particularly from botnet attacks that disrupt system stability. Addressing this issue requires effective strategies to control botnet propagation in social IoT environments. This study develops a social IoT botnet propagation model incorporating social factors to analyze their influences on its propagation dynamics. Based on this, a social IoT botnet propagation control framework is constructed, formulating an optimization problem using Markov games. To solve the optimization problem, we propose SD-DRQN (Social-Dynamics Deep Recurrent Q-Network), a novel deep reinforcement learning algorithm that integrates Long Short-Term Memory (LSTM) layers to improve learning in dynamic social IoT environments. Experimental results validate the performance of the proposed SD-DRQN across various social IoT scenarios, including complex real-world topologies. The algorithm demonstrates faster convergence, superior generalization, and practical applicability, making it an effective solution for botnet propagation control in real-world social IoT deployments.

Federated learning (FL) allows multiple devices to train a high-performance global model cooperatively without sharing their private data. One of the key challenges in FL for Internet of Things (IoT) scenarios is that the statistical heterogeneity in local data distribution for IoT devices, which will cause the low quality of local models, degrading the performance of the global model. To address this problem, focusing on improving the quality of local models, we propose a match-based hierarchical FL framework (FedAvg-Match), in which two IoT devices with complementary datasets are formed as a training group to reduce the influence of data heterogeneity. By introducing Earth mover’s distance for data distribution, we design an improved Gale-Shapley algorithm with time complexity$O(N^{2}\log N)$for IoT device grouping, which can obtain a stable matching. Experimental results show that, compared to the ungrouped FedAvg algorithm, the proposed FedAvg-Match method significantly improves both the accuracy of the global model and the convergence speed of the training process, while also reducing communication costs.

In the Social Internet of Things (SIoT) environment, malware propagation is attracting more and more attention due to increasing damages. Markov chain models have been used to predict epidemic behavior qualitatively and quantitatively, but most of them model random propagation as a basic multiplicative factor. In this article, we propose an epidemic model Susceptible-Infected without command-Infected with command$(SII^{\prime })$, and derive an exact Markov chain for SIoT malware propagation. We also employ a Markov chain for an SIoT malware mitigation system that groups random devices alongside those with detected infections during the malware eradication process. This mitigation mechanism operates at the network scale, addressing the risks associated with large-scale SIoT deployments through a strategic, yet assertive, approach of widespread disconnections. Such a system effectively drives down the basic reproduction number to less than 1, preventing malware from gaining dominance over the network—all accomplished without modifying the recovery rate. We conducted experimental simulations of the proposed model’s dynamic predictions, and the experimental results show that the use of an exact Markov chain model better matches the benchmark results of our proposed model and also verifies the different effects of group-based mitigation in different SIoT contexts.

Defending Edge intelligence-based Internet of Things (EIoT) systems by controlling malware propagation has become a critical issue. Herein, we meet new challenges brought by multiple attackers and defenders for effective mitigation of malware propagation in EIoT. To explore the dynamic changes of malware propagation, a state transition diagram of IoT nodes is proposed to describe the mutual changes of five states: infected, active, dormant, isolated, and hardened, and differential equations for each state are established. We then build a mean-field game model representing the interactions between multiattackers and multidefenders in the EIoT malware propagation defense environment. We further convert the problem of solving the game into an MDP (Markov Decision Process) and propose a distributed algorithm called MFGA3C (Mean-Field Game-based Asynchronous Advantage Actor-Critic) that combines mean-field game and multiagent asynchronous advantage actor-critic to learn the optimal defense policy. Finally, we compare our algorithm MFGA3C with other benchmark algorithms in the EIoT malware propagation countermeasure environment. Experimental results show that our MFGA3C algorithm dominates the average reward, total reward, and the number of successful defenses under three typical EIoT-environmental conditions, which indicates that MFGA3C faster and more robustly learns the optimal malware propagation defense policy, contributing to protecting EIoT systems.

The end-edge-cloud-based Social Internet of Things (SIoT) faces increasing threats from malware. To address these challenges, we propose an explainable novel malware defense framework that integrates Markov games with multi-agent deep reinforcement learning under an end-edge-cloud-based SIoT collaborative architecture. The framework models the interactions between malicious SIoT nodes and edge devices as a multi-agent game problem, incorporating multi-layer defense mechanisms to achieve precise descriptions of attack-defense behaviors. By combining Markov games with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, we develop the Robust Multiagent Actor-Critic (RMAAC) algorithm, which enables adaptive strategy optimization. To enhance system interpretability, we introduce SHapley Additive exPlanations (SHAP) value analysis into the defense decision-making process, providing transparent insights into feature contributions and decision rationales. Extensive experimental results demonstrate that the proposed RMAAC algorithm significantly outperforms existing methods, including MADDPG and Minimax Multi-Agent Deep Deterministic Policy Gradient (M3DDPG), in multiple performance metrics such as episode reward, hacking success rate, and cumulative defense number. Through systematic parameter optimization, including batch size and agent interaction speed, the framework provides an effective and sustainable solution for explainable malware defense in SIoT environments.

Malware in the Internet of Things (IoT) is prone to contaminating various IoT end-points through network communication and information transfer, leading to surreptitious privacy leakage and data theft. The existing privacy-preserving approaches including data masking, anonymization, and differential privacy always lack the consideration of strategic interactions among rational agents. Inspired by Bayesian games, we model incomplete stochastic games between IoT end-points and edge nodes in edge intelligence (EI)-enabled IoT systems to conduct probability analysis for predicting and defending privacy leakage caused by malware infection. It is notable that the posterior probability is defined based on the Bayes’ rule to reflect the statistical inference of incomplete privacy leakage information. Such a method can intrinsically characterize the actual situations of IoT end-points. Further, we propose a novel privacy preservation optimization approach named Bayesian advantage actor critic (BA2C) for the practical implementation of optimization decision in EI-enabled IoT privacy-preserving systems. Eventually, we conduct experimental simulations to understand the most effective parameters in decision-making among the successful detection rate, successful infection rate, and false alarm rate. We also compare traditional algorithms and validate the efficacy of the proposed approach.

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning. However, it suffers from sampling bias, which hinders its performance. While the mainstream solutions, hard negative mining (HNM) and supervised CL (SCL), have been proposed to mitigate this critical issue, they do not effectively address graph CL (GCL). To address it, we propose graph positive sampling (GPS) and three contrastive objectives. The former is a novel learning paradigm designed to leverage the inherent properties of graphs for improved GCL models, which utilizes four complementary similarity measurements, including node centrality, topological distance, neighborhood overlapping, and semantic distance, to select positive counterparts for each node. Notably, GPS operates without relying on true labels and enables preprocessing applications. The latter aims to fuse positive samples and enhance representative selection in the semantic space. We release three node-level models with GPS and conduct extensive experiments on public datasets. The results demonstrate the superiority of GPS over state-of-the-art (SOTA) baselines and debiasing methods. In addition, the GPS has also been proven to be versatile, adaptive, and flexible.

The rise of the industrial Internet of Things (IIoT) has enhanced industrial processes through interconnected devices and data exchange, but it also introduces significant security vulnerabilities, such as malware attacks, which threaten system reliability and availability. To address this challenge, we extend the traditional reliability block diagram (RBD) method by integrating stochastic games to evaluate the security and availability of IIoT systems. Our approach constructs a comprehensive Markov transition matrix using additional node states, enabling detailed simulations of malware spread in IIoT networks. By modeling the interactions between malware and IIoT systems through stochastic games, we propose an innovative reinforcement learning algorithm named evaluation-driven Q-learning (EDQL) to solve these complex scenarios. This novel application of EDQL in the realm of availability evaluation is a significant contribution, providing a rare integration of game theory into this field. We also derive the availability of individual IIoT nodes using reliability theory and integrate these insights into the RBD framework. Experimental results demonstrate that the EDQL algorithm significantly outperforms traditional reinforcement learning methods in malware reward. Furthermore, our method effectively evaluates common IIoT topologies and offers practical deployment recommendations, highlighting its practical impact and significance in enhancing IIoT system security and availability.

Internet of Things (IoT) systems are susceptible to compromise due to malware propagation, leading to the data breach and information theft. In this paper, we propose a proactive deception-oriented hypergame-theoretic malware propagation-mitigation (DHMPM) model between IoT nodes and edge devices under asymmetric information in edge intelligence (EI)-enabled IoT systems. We then explore malware-propagated deceptive defense strategies based on deep reinforcement learning. Specifically, IoT nodes and edge devices continually adjust their strategies based on obtained utilities under beliefs perceived by uncertainties from the game environment and system dynamics. Built upon the proposed game DHMPM, we next apply spiking neural networks (SNNs) into deep Q-network to form hypergame-theoretic deep spiking Q-network (HGDSQN), practically converging to the optimal malware-propagated deceptive defense strategy in EI-enabled IoT systems. Such SNNs can simulate biological brains with the pulse communication mechanism and break through the bottleneck of temporal processing in traditional models with deep neural networks, realizing intelligent decision-making and real-time malware defense. We eventually perform experimental simulations that assess the effect of attack arrival probability and learning rate on the optimal learning strategy selection, demonstrating the effectiveness of the proposed HGDSQN algorithm.

Distributed Denial-of-Service (DDoS) attacks pose a serious threat to the stability and security of edge intelligence devices. To solve this issue, we first describe the cost of edge intelligence environments in detail and introduce the mean-field term, edge intelligence repair speed, and DDoS attack intensity, which provide a theoretical basis for the subsequent model construction. Secondly, based on Hamilton-Jacobi-Bellman (HJB) backward and Fokker-Planck-Kolmogorov (FPK) forward equations, a mean-field model is proposed. The optimal DDoS defense policy is then solved considering the strongest DDoS attack intensity and the best edge intelligence device repair speed. Further, the focus is turned to the interaction between DDoS attackers and the edge intelligence environment. We give the update rule of the mean-field term and construct a mean-field game (MFG) model with a value function. Finally, we propose a multi-agent deep reinforcement learning algorithm called Mean-Field Games with Dueling Double Deep Q-learning Network (MFGD3QN) to solve the optimal DDoS defense policy problem under the MFG model. In the experiment, we compare MFGD3QN with several benchmark algorithms and verify the superiority of the MFGD3QN algorithm in an edge intelligence environment. We also carry out experiments on different parameters of the MFGD3QN algorithm, lower visibility conditions of the edge intelligence environment, and different repair speeds of edge intelligence devices, which verify the robustness and feasibility of the algorithm.

The Internet of Things (IoT) is used in homes and hospitals and deployed outdoors to control and report environmental changes, prevent fires, and perform many more beneficial functions. However, all these benefits come at the tremendous risk of loss of privacy and security issues. To protect the IoT, much research has been carried out to address these risks and find better ways to eliminate them or at least minimize their impact on user privacy and security requirements. This paper expounds various network security risks faced by the IoT, analyzes their impacts, discusses risk assessment methods, shows the causes and hazards of these threats, and proposes an overall framework of privacy security protection. This paper summarizes the typical defect types in the implementation of IoT firmware, analyzes the generation mechanism of typical defects from the perspectives of fuzzy testing, program verification and machine learning, and compares and expounds the progress of security research for several common IoT protocols. This paper analyzes and summarizes the mainstream access control model in the existing IoT and the access control model after using the blockchain and builds a new integrated AIoT architecture for intelligent information processing. Finally, this paper expounds on the current legal development status of the privacy protection of network information in various countries and discusses the future prospects of the IoT.

Opportunistic computation offloading is an effective way to improve the computing performance of Industrial Internet of Things (IIoT) devices. However, as more and more computing tasks are being offloaded to mobile-edge computing (MEC) servers for processing, it can lead to IIoT privacy and security issues, such as personal usage habits. In this paper, we aim to design a Lyapunov-based privacy-aware framework that defines the amount of IIoT user privacy and designs a “reduced amount of privacy” mechanism. We first define the cumulative privacy amount for each IIoT user and trigger the privacy protection mechanism when the cumulative privacy amount exceeds the set privacy threshold. The offloading data generated by the IIoT user is then transferred to local processing, and finally, the cumulative privacy amount of the IIoT user is reduced. This model ensures that the cumulative privacy of all IIoT users remains stable. We further combine the advantages of Lyapunov optimization and actor-critic networks to address the problem of how to make the model learn the optimal policy and maintain the minimum energy consumption in the long run. Especially, this framework integrates model-based optimization and model-free actor-critic networks to handle the offloading problem with very low computational complexity, and Lyapunov optimization ensures that this framework minimizes energy consumption while stabilizing the data queue. It is demonstrated through experimental simulation results that the proposed scheme can maintain data queue stability and minimize energy consumption under strict security.

The Industrial Internet of Things (IIoT) has experienced rapid growth in recent years, with an increasing number of interconnected devices, thereby expanding the attack surface. Effectively detecting intrusions is crucial for safeguarding IIoT systems from malicious attacks. However, due to the dynamic and complex nature of the IIoT environment, designing an intrusion detection strategy that balances accuracy and efficiency remains a significant challenge. In this paper, we propose a novel intrusion detection strategy based on stochastic games and deep reinforcement learning (DRL) for detecting attacks effectively while balancing detection accuracy and efficiency in the IIoT. We model the interaction between attackers and detectors as dynamic adversarial stochastic games with incomplete information, theoretically analyze Nash equilibria, and construct a node-based simulation of interconnected infrastructure within the IIoT. We then propose a novel algorithm DDQN-LP combining Double Deep Q-Networks with “lazy penalty” to determine optimal strategies and encourage agents to promptly conclude the game to reduce overhead. Furthermore, we identify different optimal hyperparameters for training our DRL agents and evaluate their efficacy both theoretically and empirically. We compare our proposed algorithm with other reinforcement learning algorithms, and simulations demonstrate our approach has better performance with a higher detection rate as well as lower consumption.

Industrial Internet of Things (IIoT) has brought a lot of convenience for the industrial world to digitization, automation and intelligence, but it inevitably introduces inherent cyber security risks, resulting in an issue that traditional intrusion detection techniques are no longer sufficient for IIoT environments. To solve this issue, we propose an open-set solution called DC-IDS for IIoT based on deep reinforcement learning. In this solution, the open-set recognition problem in intrusion detection is modeled as a discrete-time Markov decision process, and Deep Q-Network (DQN) is employed to solve it. Meanwhile, a Conditional Variational Auto-Encoder is introduced to the value network in DQN. Therefore, the open-set recognition problem in intrusion detection is divided into two subproblems, namely known traffic fine-grained classification problem and unknown attacks recognition problem. We use DQN to solve the known traffic fine-grained classification problem. Since the reconstruction error of known traffic is generally smaller than the reconstruction error of unknown attacks, we use reconstruction error to recognize unknown attacks. Experiments on IIoT dataset TON-IoT demonstrate the effectiveness of DC-IDS model, which achieves better performance in terms of the recognition rate of unknown attacks as well as the stability of the model compared to previous proposed methods.

Abstract Currently, the accurate prediction of social relationships can effectively reduce the decision‐making burden of users in various service platforms. However, in the big data environment, the users' data information used for the relationship prediction is highly fragmented distribution, so it is a non‐trivial challenge to integrate the users' sequence data information from different platforms while preventing sensitive information leakage. To this end, based on the microservice environment, we devise a cross‐platform social relationship prediction approach (CPSRP) to address the above problems. Briefly, the improved Simhash method aggregates similar users into the common bucket. Then the embedding technique converts the users' sparse data information into the low‐dimensional dense continuous feature vectors; the redefined Gated Recurrent Unit (r‐GRU) network and the Multilayer Perceptron (MLP) network are employed to extract the overall temporal sequence features of users. The relationship prediction is finally executed according to the users' sequential features. Extensive experiments are conducted on Epinions, and the experimental results further prove the benefits of our proposal in terms of relationship prediction while protecting users' sensitive information.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (namedMHDP$_{SVD\_{A}RIMA}$) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show thatMHDP$_{SVD\_{A}RIMA}$approach is effective and efficient in predicting missing healthcare data.

Malware propagation in IoT (Internet of Things) systems can lead to data leakages, financial losses, and other serious consequences. To solve this issue, we propose a new active IoT malware propagation defence work. Specifically, aided by stochastic games, we express the process of cyber conflicts between IoT system nodes and edge devices considering malware propagation in edge intelligence-enabled IoT. Here, IoT system nodes and edge devices choose their own strategies and receive the corresponding rewards determined by the current state and strategy. After that, the game randomly moves to the next stage according to the distribution of probabilities and the participants’ strategies until reaching the fixed Nash equilibrium point. Following a theoretical analysis, we design and implement SGD3QN (Stochastic Games and Dueling Double Deep Q-networks)—a novel algorithm to receive the optimal strategy for mitigating IoT malware propagataion in practice. Here, the Dueling Double Deep Q-networks are acted as an end-to-end decision control system, in which IoT malware propagataion environment is used as the input to obtain the failure or success experience to update the network parameters, followed by making the optimal decision output. Afterwards, we perform experimental simulations that probe the influence of batch size and replay memory size on the optimal IoT malware propagation defense strategy selection and prove the ascendancy of the proposed SGD3QN-aided decision-making algorithm.

Smart devices (SDs) used in the Industrial Internet of Things can generate computational tasks for processing the data generated during production. However, due to the limited processing power of SDs, it is necessary to transfer these computational tasks to more powerful devices for processing. To this end, we propose a Mobile Edge Computing (MEC) system based on a Software Defined Network (SDN) for SDs to offload their computational tasks. This MEC system includes multiple MEC servers to handle numerous SDs, which leads to load-balancing challenges among these servers. To tackle this problem, we develop a computational offloading model based on mean-field game theory and introduce a mean-field game-based load-balancing algorithm (MFGLB), which reduces processing latency and facilitates task scheduling through Multi-Agent Deep Reinforcement Learning. Each SD in the MEC system is considered a participant in the mean-field game, simplifying the complex stochastic game into a more manageable dual-agent game. We then prove the existence of Nash Equilibrium for this mean-field game. To evaluate the effectiveness of our MFGLB algorithm, we compare its performance with traditional load-balancing algorithms and a stochastic game-based load-balancing algorithm. Our experimental results demonstrate the superiority of MFGLB in reducing processing latency and addressing load imbalances.

The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we concern about the privacy protection in the MEC system with a curious edge server. We present a deep reinforcement learning (DRL)-driven computation offloading strategy designed to concurrently optimize privacy protection and computation cost. We investigate the potential privacy breaches resulting from offloading patterns, propose an attack model of privacy theft, and correspondingly define an analytical measure to assess privacy protection levels. In pursuit of an ideal computation offloading approach, we propose an algorithm, SAC-PP, which integrates actor-critic, off-policy, and maximum entropy to improve the efficiency of learning processes. We explore the sensitivity of SAC-PP to hyperparameters and the results demonstrate its stability, which facilitates application and deployment in real environments. The relationship between privacy protection and computation cost is analyzed with different reward factors. Compared with benchmarks, the empirical results from simulations illustrate that the proposed computation offloading approach exhibits enhanced learning speed and overall performance.

Space-aerial-ground integrated networks (SAGIN) is gaining eye-catching attention in 6G research. Comparing with terrestrial networks, SAGIN guarantees to provide three-dimensional (3D), seamless connectivity, global coverage and high resource usage efficiency. In addition, network softwarization (NetSoft) is recognized as the crucial attribute of 6G networks. With softwarization, traditional dedicated hardware will be decoupled into software blocks and general-purpose hardware. Tailored service requests can be implemented in the forms of chained software blocks (also called as slices) and coexist on top of these general-purpose hardware. The softwarization scheme can enhance the resource utilization and service diversity. Though SAGIN and NetSoft are separately studied well, their joint research is still in its infancy. In this paper, we focus on the research of softwarized SAGIN and propose one novel resource allocation and orchestration framework, labeled as Stice-Soft-SAGIN. The goal of our Stice-Soft-SAGIN framework is to provide reliable and efficient slicing service in quasi-static state. When receiving one slicing service request, our Slice-Soft-SAGIN will conduct the first procedure of available resource checking. After successfully doing the resource checking, our Stice-Soft-SAGIN will turn to conducting the slicing resource allocation and orchestration from three ordered parts (terrestrial part, aerial part, and satellite part). Take note that resources considered in Stice-Soft-SAGIN belong to wireless (spectrum) and wired (computing and storage) types. In order to validate the Stice-Soft-SAGIN, we conduct the evaluation in the simulation form. Evaluation results are illustrated and analyzed.

Inspired by the success of Contrastive Learning (CL) in computer vision and natural language processing, Graph Contrastive Learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debias- ing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets. To enhance the repro- ducibility of our work, we make all the code publicly available at https://github.com/Zehong-Wang/HGCML.

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose a software-defined network (SDN)-based mobile edge computing (MEC) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDN’s programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Second, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multiuser stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PERs) to minimize energy consumption and processing latency with Multiagent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG,$Q$-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.

In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.

The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API functions and API quality performances (e.g., popularity, compatibility, and diversity) simultaneously. These decrease the mashup development success rate and the mashup developers’ satisfaction significantly. In view of these challenges, a novel web APIs’ recommendation method named the popularity-aware and diverse method of web API compositions’ recommendation (PD-WACR) is proposed in this article. In concrete, we model web APIs’ functions, popularity, and compatibility with an API correlation graph. Afterward, correlation graph-based web APIs’ recommendation is performed with popularity and compatibility guarantee. Moreover, a top-$k$strategy is adopted in the recommendation process, so as to diversify the final recommended web APIs’ results. Finally, massive experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com. Experimental comparisons with related methods show the advantages and innovations of the proposed PD-WACR method.

In cryptocurrencies, privacy of users is preserved using pseudonymity . However, it has been shown that pseudonymity does not result in anonymity if a user's transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted third–party entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this article, we propose Unlinkable Coin (UCoin), a secure mix–based approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDC–net, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDC–net protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted third–party, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.

Industrial Internet of Things (IIoT), which has the capability of perception, monitoring, communication and decision–making, has already exposed more security problems that are easy to be invaded by malware because of many simple edge devices that help smart factories, smart cities and smart homes. In this paper, a two–layer malware spread–patch modelIIPVis proposed based on a hybrid patches distribution method according to the simple edge equipments and limited central computer resources of IIoT. The spread process of malware in IIoT was deeply analyzed using differential game and a differential game model was established. Then optimization theory was further used to solve the optimization problem extracted by introducing subjective effort parameters to obtain the optimal control strategies of devices for malware and patches. In addition, we combined the deep reinforcement learning algorithm into the modelIIPVto design a new algorithmDDQN–PVsuitable for suppressing the spread of malware in IIoT during the experiments. Finally, the effectiveness of modelIIPVand algorithmDDQN–PVare verified by numerous comparative experiments.

In order to better assist the rehabilitation treatment of patients with musculoskeletal injury, standard rehabilitation actions are needed to guide the musculoskeletal rehabilitation process. With more and more urgent demands, the musculoskeletal rehabilitation evaluation systems have attracted a high degree of attention. Experts have proposed a series of systems based on laser, ultrasound, and image, which can give reasonable recognition and judgment. However, these systems either require specialized and expensive equipment or can be affected by ionizing radiation. How to construct a musculoskeletal rehabilitation evaluation system with low cost, good effect, and little injury is still a great challenge. In this article, we propose MSEva, a musculoskeletal rehabilitation evaluation system based on EMG signals. Specifically, the system uses EMG sensors to collect a large amount of data for five rehabilitation actions. Secondly, MSEva uses Wavelet Transform (WT) to extract the signal features and then puts the processed data into the Long Short-Term Memory (LSTM) network for model training. Finally, the system uses the LSTM model to evaluate the normality of the EMG response of rehabilitation actions. The results show that the average accuracy of MSEva reaches 94.37%, which has important evaluation value in guiding the rehabilitation of musculoskeletal patients.

Summary With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource‐rich cloud centers have been utilized to tackle these challenges. To relieve the burden on cloud centers, edge‐cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and quality of service. Several optimization models of edge‐cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and cost‐oriented computation offloading optimization modelPECCOproposed in this article. Considering that the model is hard in nature and the optimization objective is not differentiable, we propose an improved Moth‐flame optimizerPECCO‐MFIwhich addresses some deficiencies of the original Moth‐flame optimizer and integrate it under the edge‐cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimizing the proposed task offloading model under the edge‐cloud environment.

To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.

Secure frequency control of multi-area hybrid power systems with wind power is a research problem involving active defense, vulnerability, and resilience. Considering the scenario that Denial-of-Service (DoS) attack intrude into the control channels of thermal power and wind farm, the hybrid power system is modeled by a switched system with four subsystems. Then, the exponential stability of hybrid power system is studied under arbitrary DoS attack. An active defense scheme is proposed to design switched control gains by Lie algebra method achieved by a distributed consensus method. Furthermore, following the resulted exponential stability, the load disturbance attenuant performance of frequency control is studied by the proposed concepts of vulnerability point and resilience point. Under a class of event-DoS attack model, the estimation method of vulnerability point and resilience point is given. Finally, simulations of a three-area hybrid power system and NE39bus test system are carried out to verify our theories.

Internet of Things (IoT) anomaly detection is significant due to its fundamental roles of securing modern critical infrastructures, such as falsified data injection detection and transmission line faults diagnostic in smart grids. Researchers have proposed various detection methods fostered by machine learning (ML) techniques. Federated learning (FL), as a promising distributed ML paradigm, has been employed recently to improve detection performance due to its advantages of privacy-preserving and lower latency. However, existing FL-based methods still suffer from efficiency, robustness, and security challenges. To address these problems, in this article, we initially introduce a blockchain-empowered decentralized and asynchronous FL framework for anomaly detection in IoT systems, which ensures data integrity and prevents single-point failure while improving the efficiency. Further, we design an improved differentially private FL based on generative adversarial nets, aiming to optimize data utility throughout the training process. To the best of our knowledge, it is the first system to employ a decentralized FL approach with privacy-preserving for IoT anomaly detection. Simulation results on the real-world dataset demonstrate the superior performance from aspects of robustness, accuracy, and fast convergence while maintaining high level of privacy and security protection.

With the rapid development of wireless communication, traditional cloud computing cannot fully support low-latency services, especially in sensor networks. Mobile edge computing (MEC) can improve the quality of experience of end users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, it may cause discontinuity of services if these mobile end devices roam around different MEC servers’ areas. To solve the aforementioned problem, in this article, we propose an effective edge-intelligent service placement algorithm (EISPA), which transforms the service placement problem into finding a globally optimal solution via nature-inspired particle swarm optimization (PSO). Moreover, we use a shrinkage factor and combine it with the simulated annealing (SA) algorithm to adjust the position of particles in our algorithm, which aims to avoid falling into an optimal local solution to a certain extent. Performance analysis results show that the EISPA is approaching the optimal enumeration collaborative computation offloading algorithm, and system cost under energy constraints is 83.6%, 20.4%, and 20.3% lower than that in Only Local, Finding the Nearest Edge, and the genetic SA-based PSO algorithms, respectively, which proves that the EISPA has better performance.

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

Resilient distributed coordination control is studied on multiarea power systems with low inertia under hybrid attacks, including denial-of-service (DoS) attack and deception attack. The communication among various areas under the DoS attack is deteriorated to switching residual topologies whose time characteristic is modeled by model-dependent average dwell time (MDADT). Deception attack with malicious strategy targeting at negative feedback control is modeled by a sign function. To obtain resilience performance of the power system under low inertia and hybrid attacks, resilient distributed scheme combining load-frequency control (LFC) with virtual inertia control (VIC) is proposed. Then, resilient frequency control problem of the studied power system is converted to$H_{\infty }$control of the switched nonlinear system. By employing the Lyapunov stability theory and switched system method, the resilient conditions are given by the lower bound of the average dwell time of each residual topology and the upper bound of deception attacks. Furthermore, a linear matrix inequality (LMI) technique is used to design the distributed resilient control gains of the LFC-VIC scheme. Finally, a simulation of four-area power systems is carried out to verify the validness of our theory.

Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

To enhance dependable resource allocation against increasing distributed denial-of-service (DDoS) attacks, in this article, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker's objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.

We consider the Internet of Things (IoT) with malware diffusion and seek optimal malware detection strategies for preserving the privacy of smart objects in IoT networks and suppressing malware diffusion. To this end, we propose a malware detection infrastructure realized by an intrusion detection system (IDS) with cloud and fog computing to overcome the IDS deployment problem in smart objects due to their limited resources and heterogeneous subnetworks. We then employ a signaling game to disclose interactions between smart objects and the corresponding fog node because of malware uncertainty in smart objects. To minimize privacy leakage of smart objects, we also develop optimal strategies that maximize malware detection probability by theoretically computing the perfect Bayesian equilibrium of the game. Moreover, we analyze the factors influencing the optimal probability of a malicious smart object diffusing malware, and factors influencing the performance of a fog node in determining an infected smart object. Finally, we present a framework to demonstrate a potential and practical application of suppressing malware diffusion in IoT networks.

Sensor-cloud computing is envisioned as a promising technology that can integrate various services by extending the computational capabilities of physical sensor nodes. It is prone to attack because of special characteristics of physical sensor nodes and virtual sensor-service nodes. Considering the intrusion detection threshold, false alarm probability of the intrusion detection system (IDS), and three different attacked scenarios, we devise a physical IDS (PIDS)-to-gateway and virtual IDS (VIDS)-to-gateway detection model for Sensor-Cloud. We formulate a two-layer gateway-assisted detection and defense decision problem involving multiple IDSs using an evolutionary game in order to optimize the intrusion detection strategy for lowering energy consumption and reducing alarm messages. We derive an evolutionary stable strategy and prove that the proposed mechanism achieves Nash equilibrium, such that each IDS completes cooperatively defense tasks. We propose a game-theoretic approach to achieve an energy-efficient cooperative defense scheme for sensor-cloud computing environments. The simulation results demonstrate that the proposed mechanism achieves energy-efficient defense and increases security of data in the Sensor-Cloud.

Wireless sensor networks (WSNs) are prone to propagating malware because of special characteristics of sensor nodes. Considering the fact that sensor nodes periodically enter sleep mode to save energy, we develop traditional epidemic theory and construct a malware propagation model consisting of seven states. We formulate differential equations to represent the dynamics between states. We view the decision-making problem between system and malware as an optimal control problem; therefore, we formulate a malware-defense differential game in which the system can dynamically choose its strategies to minimize the overall cost whereas the malware intelligently varies its strategies over time to maximize this cost. We prove the existence of the saddle-point in the game. Further, we attain optimal dynamic strategies for the system and malware, which are bang-bang controls that can be conveniently operated and are suitable for sensor nodes. Experiments identify factors that influence the propagation of malware. We also determine that optimal dynamic strategies can reduce the overall cost to a certain extent and can suppress the malware propagation. These results support a theoretical foundation to limit malware in WSNs.