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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular Networking

IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

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HMCGeo: IP Region Prediction Based on Hierarchical Multi-label Classification

arXiv

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arXiv 2025年

作者： Zhao, Tianzi Liu, Xinran Zhang, Zhaoxin Zhao, Dong Li, Ning Zhang, Zhichao Wang, Xinye School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Tianhe Cyberspace Security Technology Research Institute Co. Ltd. Weihai China

Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods are regression-based;however, due to noise in the input data, these methods typically encounter kilometer-level prediction errors and provide incorrect region information for users. To address this issue, this paper proposes a novel hierarchical multi-label classification framework for IP region prediction, named HMCGeo. This framework treats IP geolocation as a hierarchical multi-label classification problem and employs residual connection-based feature extraction and attention prediction units to predict the target host region across multiple geographical granularities. Furthermore, we introduce probabilistic classification loss during training, combining it with hierarchical cross-entropy loss to form a composite loss function. This approach optimizes predictions by utilizing hierarchical constraints between regions at different granularities. IP region prediction experiments on the New York, Los Angeles, and Shanghai datasets demonstrate that HMCGeo achieves superior performance across all geographical granularities, significantly outperforming existing IP geolocation methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Cross-Layer-Optimized Link Selection for Hologram Video Streaming Over Millimeter Wave Networks

Cross-Layer-Optimized Link Selection for Hologram Video Stre...

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IEEE Conference on Wireless Communications and Networking

作者： Yiming Jiang Yanwei Liu Jinxia Liu Antonios Argyriou Yifei Chen Wen Zhang State Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhejiang Wanli University Ningbo China Department of Electrical and Computer Engineering University of Thessaly Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Holographic-type communication brings an immersive tele-holography experience by delivering holographic contents to users. As the direct representation of holographic contents, hologram videos are naturally three-dimensional representation, which consist of a huge volume of data. Advanced multi-connectivity (MC) millimeter-wave (mmWave) networks are now available to transmit hologram videos by providing the necessary bandwidth. However, the existing link selection schemes in MC-based mmWave networks neglect the source content characteristics of hologram videos and the coordination among the parameters of different protocol layers in each link, leading to sub-optimal streaming performance. To address this issue, we propose a cross-layer-optimized link selection scheme for hologram video streaming over mmWave networks. This scheme optimizes link selection by jointly adjusting the video coding bitrate, the modulation and channel coding schemes (MCS), and link power allocation to minimize the end-to-end hologram distortion while guaranteeing the synchronization and quality balance between real and imaginary components of the hologram. Results show that the proposed scheme can effectively improve the hologram video streaming performance in terms of PSNR by 1.2 dB to $\mathbf{6. 4 d B}$ against the non-cross-layer scheme.

关键词： Video coding Cross layer design Bit rate Streaming media Distortion Synchronization Resource management Millimeter wave communication Streams Videos

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Cross-Layer-Optimized Link Selection for Hologram Video Streaming over Millimeter Wave Networks

arXiv

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arXiv 2025年

作者： Jiang, Yiming Liu, Yanwei Liu, Jinxia Argyriou, Antonios Chen, Yifei Zhang, Wen State Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhejiang Wanli University Ningbo China Department of Electrical and Computer Engineering University of Thessaly Greece

Holographic-type communication brings an immersive tele-holography experience by delivering holographic contents to users. As the direct representation of holographic contents, hologram videos are naturally three-dimensional representation, which consist of a huge volume of data. Advanced multi-connectivity (MC) millimeter-wave (mmWave) networks are now available to transmit hologram videos by providing the necessary bandwidth. However, the existing link selection schemes in MC-based mmWave networks neglect the source content characteristics of hologram videos and the coordination among the parameters of different protocol layers in each link, leading to sub-optimal streaming performance. To address this issue, we propose a cross-layer-optimized link selection scheme for hologram video streaming over mmWave networks. This scheme optimizes link selection by jointly adjusting the video coding bitrate, the modulation and channel coding schemes (MCS), and link power allocation to minimize the end-to-end hologram distortion while guaranteeing the synchronization and quality balance between real and imaginary components of the hologram. Results show that the proposed scheme can effectively improve the hologram video streaming performance in terms of PSNR by 1.2dB to 6.4dB against the non-cross-layer scheme. Copyright © 2025, The Authors. All rights reserved.

关键词： Holograms

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DEEPCON: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Qian, Bin Qian, Jiaxu Wen, Zhenyu Wu, Di He, Shibo Chen, Jiming Ranjan, Rajiv Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China University of St Andrews School of Computer Science United Kingdom Newcastle University United Kingdom

In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.

关键词： Distributed cloud

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Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction

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computers in Biology and Medicine 2025年 184卷 109321-109321页

作者： Xuan, Ping Wu, Shien Cui, Hui Li, Peiru Nakaguchi, Toshiya Zhang, Tiangang Department of Computer Science and Technology Shantou University Shantou China School of Cyberspace Security Hainan University Haikou China Department of Computer Science and Information Technology La Trobe University Australia Australian Centre for AI in Medical Innovation La Trobe University Australia School of Computer Science and Technology Heilongjiang University Harbin China Center for Frontier Medical Engineering Chiba University Japan

Identifying the potential side effects for the interested drugs can help reduce harm to patients caused by drugs in clinical use and decrease the risk of drug development failure. Multiple functionally similar drugs often have multiple similar side effects, resulting in the closed relationships among these nodes. However, most of previous methods did not completely encode the features from the biological perspective to mine the complex associations between the drugs and side effects. A prediction model based on interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning, ICAL, was proposed to fuse the global correlations reflected by multiple hypergraphs and to learn the attributes of a pair of drug and side effect nodes enhanced by the channels and attributes. First, we designed a hypergraph architecture where a hyperedge reflects the complex correlations between a single drug (side effect) and all the drugs and side effects, and the entire hypergraph composed of the hyperedges reveals the global correlations of all the drugs and side effects. Two hypergraphs were established based on two types of drug similarities, and each hypergraph implies its specific complex relationships among multiple drugs and side effects. Second, we proposed an interactive hypergraph neural network to enable the learning of global correlation features of drugs and side effects from the two hypergraphs. It propagated the node features across multiple hypergraphs and encoded the context relationships within these hypergraphs. Besides, the attentions at the channel level and at the attribute level were proposed to integrate the semantic correlations among multiple channels and to encode the long-distance dependence within the attributes of a pair of drug and side effect. The experimental results based on cross-validation showed that our new model outperformed seven advanced prediction methods in terms of AUC, AUPR, and recall rates for the top-ranked candidat

关键词： Prediction models

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Constraint-Pareto Dominance and Diversity Enhancement Strategy based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Liu, Zhe Han, Fei Ling, Qinghua Han, Henry Jiang, Jing Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Hefei Comprehensive National Science Center Anhui Energy Laboratory Institute of Energy China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China Jiangsu University of Science and Technology School of Computer Science Zhenjiang212003 China Baylor University School of Engineering and Computer Science Waco76798 United States Anqing Normal University School of Computer and Information Anqing246011 China

The utilization of both constrained and unconstrained-based optimization for solving constrained multi-objective optimization problems (CMOPs) has become prevalent among recently proposed constrained multiobjective evolutionary algorithms (CMOEAs). However, the constrained-based optimization which adopted by many CMOEAs typically gives priority to feasible solutions over infeasible ones regardless of their objective values, potentially leading to degraded performance due to the elimination of promising infeasible solutions with strong convergence and diversity. Furthermore, many existing CMOEAs have difficulty in maintaining diversity while focusing on feasibility, thereby hindering their ability to effectively address CMOPs characterized by complex feasible regions. To tackle these challenges, a constraint-Pareto dominance relationship is proposed in this paper to evaluate solutions based on both objectives and feasibility, to improve the optimization potential by reduce the elimination probability of promising infeasible solutions. A diversity enhancement strategy is also designed to enable simultaneously focus on both diversity and feasibility, thus effectively ensuring the diversity of the feasible solutions obtained. Empirical results from benchmark suites and real-world problems demonstrate that our proposed algorithm surpasses state-of-the-art CMOEAs. © 1997-2012 IEEE.

关键词： Optimization algorithms

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RobustPFL: Robust Personalized Federated Learning

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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Joint Optimization of Offloading and Caching in Full-Duplex-Enabled Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Dai, Xingxia Tian, Shujuan Liu, Haolin Li, Zhetao Jiang, Hongbo Deng, Qingyong Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan411105 China Jinan University College of Information Science and Technology Guangzhou510632 China Hunan University College of Computer Science and Electronic Engineering Changsha Hunan410082 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China

Edge computing (EC) reduces task processing and content download delay by providing computation and caching resources directly to task offloading (TO) users and content request (CR) users. However, existing studies often focus exclusively on either TO users or CR users within EC networks, neglecting the interaction between these two groups. To address this gap, we investigate the offloading and caching decision-making in scenarios where TO and CR users coexist. Furthermore, we employ full-duplex (FD) technology to enhance spectral utilization for edge-end transmissions. Specifically, we jointly optimize offloading and caching in FD-enabled EC networks. To accomplish this, we decompose the formulated optimization problem into three sub-problems using the alternating optimization (AO) method. We then propose a three-subproblem alternating iterative delay minimization algorithm to effectively tackle the challenges of offloading and caching. Additionally, we analyze the convergence and complexity of our proposed algorithm. Finally, we conduct extensive simulations to evaluate the effectiveness of our approach. The simulation results demonstrate that the delay reduction achieved by our algorithm is between 24.78% and 89.23% greater than that of comparative algorithms. © 2002-2012 IEEE.

关键词： Computation offloading

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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China ZJUT Binjiang Institute of Artificial Intelligence Hangzhou310056 China Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Technology Shanghai200433 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Shenzhen518066 China National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China

Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct the Transaction SubGraph Network (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%∼6.71% relative improvement over previous state-of-the-art. © 2004-2012 IEEE.

关键词： Phishing

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