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vehicular edge computing in Satellite-Terrestrial Integrated Networks

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 5290-5306页

作者： Li, Caiguo Shang, Bodong Feng, Jie Liu, Lei Chen, Shanzhi Eastern Inst Technol Eastern Inst Adv Study Ningbo 315200 Peoples R China Ningbo Inst Digital Twin Eastern Inst Technol Ningbo 315200 Peoples R China Eastern Inst Technol Eastern Inst Adv Study Ningbo 315200 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China China Informat & Commun Technol Grp Co Ltd CICT State Key Lab Wireless Mobile Commun Beijing 100191 Peoples R China

Internet of Vehicles (IoV) supported by terrestrial networks can satisfy the necessities of multiple computation-intensive applications. However, current terrestrial networks and resource management mechanisms may only partially guarantee vehicle and in-vehicle user equipment (VUE)'s quality of service due to the limited coverage of roadside units (RSU), especially in remote areas. This paper investigates vehicular edge computing (VEC) in satellite-terrestrial integrated networks with multiple low-earth orbit (LEO) satellites, ground RSUs, and VUEs. In remote areas without RSU coverage, VUEs can offload their partial tasks to satellites to save energy and guarantee latency. We aim to minimize VUEs' weighted sum energy consumption by jointly optimizing VUEs' association, data partition, computing resource allocation, power control, and bandwidth assignment under the constraints of maximum tolerant latency, maximum number of outage time slots, computation capacity at each satellite and each RSU, and maximum allowable transmission power at VUEs. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems. We efficiently solve each sub-problem by utilizing variable substitutions, the difference of convex functions algorithms, the Lagrangian dual method, and the Karush-Kuhn-Tucke conditions. Simulation results show that the introduced satellite-terrestrial integrated networks-enabled VEC scheme significantly reduces VUEs' energy consumption compared to other schemes.

关键词： Satellites Space-air-ground integrated networks Low earth orbit satellites edge computing Resource management Servers Energy consumption Bandwidth Wireless communication Simulation Internet-of-Vehicles vehicular edge computing LEO satellite resource allocation

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Joint offloading decision and resource allocation in vehicular edge computing networks

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Digital Communications and Networks 2025年第1期11卷 71-82页

作者： Shumo Wang Xiaoqin Song Han Xu Tiecheng Song Guowei Zhang Yang Yang National Mobile Communications Research Laboratory Southeast UniversityNanjing211189China College of Electronic and Information Engineering/College of Integrated Circuits Nanjing University of Aeronautics and AstronauticsNanjing211106China School of Cyber Science of Engineering Qufu Normal UniversityQufu273165China Terminus Group Beijing100027China Peng Cheng Laboratory Shenzhen518055China

With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung *** order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive ***,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face *** this paper,we propose a heterogeneous vehicular edge computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource *** the first stage,the offloading decisions of TaVs are obtained through a potential *** the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel *** simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.

关键词： Computation offloading Resource allocation vehicular edge computing Potential game Multi-agent deep deterministic policy gradient

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Adaptive Digital Twin Migration in vehicular edge computing and Networks

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IEEE TRANSACTIONS ON vehicular TECHNOLOGY 2025年第3期74卷 4839-4854页

作者： Mou, Fangyi Lou, Jiong Tang, Zhiqing Wu, Yuan Jia, Weijia Zhang, Yan Zhao, Wei Hong Kong Baptist Univ Kowloon Tong Hong Kong Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China Univ Macau State Key Lab IoT Smart City Macau 999078 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Peoples R China Univ Oslo Dept Informat N-0316 Oslo Norway Shenzhen Univ Adv Technol Shenzhen 518055 Peoples R China

The surge in mobile vehicles and data traffic in vehicular edge computing and Networks (VECONs) requires innovative approaches for low latency, stable connectivity, and efficient resource usage in fast-moving vehicles. Existing studies have identified that utilizing digital twins (DTs) can effectively improve service quality in VECONs. However, it still faces substantial challenges posed by large-scale complex DT communications in sustaining real-time collaborative endeavors. In particular, within the dynamic VECONs, the decision regarding DT migration plays a pivotal role in sustaining the quality of services. In this paper, we propose an adaptive DT migration (ADM) algorithm to minimize the overall migration costs when DTs deliver services. Specifically, 1) We formulate ADM as a combinatorial optimization problem in VECONs, comprehensively considering communication latency and migration latency under complex DT communications, vehicular mobilities, and dynamic states of edges;2) An ADM algorithm based on off-policy actor-critic reinforcement learning is proposed to make migration decisions. Moreover, the ADM agent employs warm-up policies to address exploration challenges in sparse state spaces;3) Simulations based on real-world, large-scale urban vehicular mobility datasets demonstrate that our method outperforms existing algorithms by approximately 39% on average, and it can achieve results close to the optimal.

关键词： Vehicle dynamics Real-time systems Heuristic algorithms Costs Feature extraction edge computing Approximation algorithms Adaptive systems Servers Training Digital twin migration vehicular edge computing deep reinforcement learning

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Content Caching-Assisted vehicular edge computing Using Multi-Agent Graph Attention Reinforcement Learning

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IEEE TRANSACTIONS ON vehicular TECHNOLOGY 2025年第2期74卷 3509-3514页

作者： Shen, Jinjin Lin, Yan Zhang, Yijin Zhang, Weibin Shu, Feng Li, Jun Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

In order to avoid repeated task offloading and realize the reuse of popular task computing results, we construct a novel content caching-assisted vehicular edge computing (VEC) framework. In the face of irregular network topology and unknown environmental dynamics, we further propose a multi-agent graph attention reinforcement learning (MGARL) based edge caching scheme, which utilizes the graph attention convolution kernel to integrate the neighboring nodes' features of each agent and further enhance the cooperation among agents. Our simulation results show that our proposed scheme is capable of improving the utilization of caching resources while reducing the long-term task computing latency compared to the baselines.

关键词： Vehicle dynamics Network topology edge computing Computational modeling Reinforcement learning Decision making Topology Indexes Simulation Roads edge caching graph attention reinforcement learning multi-agent vehicular edge computing

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Scheduling for Maximizing the Information Freshness in vehicular edge computing-Assisted IoT Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 4140-4151页

作者： Xie, Xin Zhong, Tao Wang, Heng Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China

vehicular edge computing (VEC), as an emerging computing paradigm, enables the timely processing of computing tasks at the network edge through on-vehicle servers, thereby meeting users' demands for information freshness. In this paper, we introduce the Age of Information (AoI) to measure information freshness and investigate the scheduling problem minimizing the long-term average AoI in VEC-assisted Internet of Things systems. The main challenge lies in the strong coupling between link scheduling and server selection under the location constraints of VEC. To address this issue, we design a scheduling strategy based on deep reinforcement learning and improve the neural network structure using a branch network approach, reducing complexity by decreasing the number of actions represented in the network's output layer. Moreover, we introduce an action masking scheme that accelerates the algorithm's convergence in this system. Numerical results show that the proposed scheduling algorithm can achieve up to a 25.4% performance gain compared to existing advanced algorithms.

关键词： Servers Optimal scheduling Internet of Things Wireless communication Job shop scheduling Indexes Complexity theory Vehicle-to-everything Streaming media Resource management Age of Information vehicular edge computing scheduling offloading

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Joint Task Offloading and Resource Allocation for Integrated VLC and Sensing in Digital Twin-Aided vehicular edge computing Networks

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IEEE TRANSACTIONS ON vehicular TECHNOLOGY 2025年第4期74卷 6360-6372页

作者： Yang, Hao-Nan Wang, Jin-Yuan Zeng, Qimiao Cheng, Ming Lin, Min Wang, Jun-Bo Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Wuhan 430070 Peoples R China China Telecom Res Inst Guangzhou 510630 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

Recently, integrated visible light communication (VLC) and sensing has emerged as a promising technology for vehicular edge computing (VEC). As the explosive increase of latency-sensitive and computation-intensive vehicular applications, limited on-board computing resources are unable to satisfy diversified requirements. This paper proposes a digital twin-aided virtualization VEC network architecture, and studies joint task offloading and resource allocation for integrated VLC and sensing. To determine the associations between channels and subtasks, we first propose a communication-sensing task offloading mechanism, where vehicles send communication and sensing data to edge servers (ESs) via the street lamps, and ESs adaptively assign subtasks for various channels. Then, we formulate a joint task offloading and resource allocation optimization problem by minimizing the overall latency. To tackle the non-convex problem, we decouple it into a communication subproblem and a sensing subproblem. We then propose a successive convex approximation and alternating minimization algorithm and an alternating minimization algorithm to solve the two subproblems, respectively. Finally, an overall algorithm framework is proposed. The convergence and complexity analysis indicates that the proposed algorithms are convergent and time-efficient. Numerical results verify the superiority of the proposed algorithms. Moreover, the effects of key parameters on latency performance are discussed.

关键词： Sensors Resource management Visible light communication edge computing Physical layer Approximation algorithms Vehicle-to-everything Minimization Radio frequency Atmospheric modeling Digital twin integrated visible light communication and sensing resource allocation task offloading vehicular edge computing

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An Efficient Partial Task Offloading and Resource Allocation Scheme for vehicular edge computing in a Dynamic Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 2488-2502页

作者： Abbas, Zahir Xu, Shihe Zhang, Xinming Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China

In conventional vehicular edge computing (VEC), vehicles at edge nodes often face issues such as congestion and overhead, particularly when numerous vehicles offload their tasks to a single edge node. This scenario results in heightened processing delays and increased energy consumption. Additionally, the unpredictability of the task offloading process at the edge node presents a major challenge for vehicles in determining their offloading strategies within a dynamic environment. In this paper, we propose a jointed approach for task offloading and resource allocation aimed at minimizing the overall latency and energy consumption of all vehicles. This is accomplished through task profiling, channel allocation, and resource distribution for both vehicles and roadside units (RSUs). We introduce a framework for partial task offloading and resource allocation based on a decentralized learning algorithm known as the Distributed Partial Task Offloading and Resource Allocation (DPTORA) scheme. This approach provides flexibility in task processing, allowing each vehicle to choose whether to execute its task locally, partially offload it, or distribute it among multiple RSUs using vehicle-to-infrastructure (V2I) connections in a dense network. We develop algorithms based on the Generalist Pursuit Learning Algorithm (GPLA) and the Distributed Partial Task Offloading (DPTO) scheme to effectively address the optimization problem. Additionally, we provide a sub-optimal solution with low computational complexity. Extensive simulations validate the effectiveness of our proposed scheme in decreasing time latency and energy consumption while facilitating partial task offloading and resource allocation through a decentralized learning Approach in dynamic VEC networks.

关键词： vehicular networks vehicular edge computing partial task offloading resource allocation decentralized learning algorithm

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A vehicular edge computing content caching solution based on content prediction and D4PG

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-15页

作者： Li, Bingxian Zhu, Lin Tan, Long Heilongjiang Univ Sch Comp Sci & Technol Harbin 150080 Heilongjiang Peoples R China

Traditional research on vehicular edge computing often ignores the challenges brought by the rapid movement of vehicles and the dynamic characteristics of the environment, and often ignores that different vehicles on the same path may generate the same computing tasks, resulting in a large number of repeated calculations. Therefore, this paper proposes a vehicular edge computing content caching solution using content prediction and D4PG. Considering the complexity of the vehicular edge environment, this study proposes a digital twin-assisted method to digitally simulate the vehicular edge environment to assist in the decision-making process related to traffic prediction and content caching strategy. In response to the problems of high-speed vehicle movement and dynamic environmental changes, this paper proposes an informer-based traffic prediction model, which uses the informer prediction model to predict the environment and provide information for vehicle task content caching. At the same time, considering the problem that different vehicles on the same path may generate the same computing tasks, this paper proposes a content caching model based on distributed deterministic policy gradient (D4PG), and uses the D4PG content caching model to determine the content caching strategy. Experimental results show that this scheme can effectively reduce the vehicle task processing delay.

关键词： vehicular edge computing Deep reinforcement learning Content caching Digital twin

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Privacy-preserving cooperative hierarchical caching approach based on federated deep reinforcement learning for vehicular edge computing

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JOURNAL OF SUPERcomputing 2025年第4期81卷 1-38页

作者： Mu, Yangxi Hao, Bing He, Mengyang Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou 450001 Henan Peoples R China Song Shan Lab Zhengzhou Henan Peoples R China

vehicular edge computing (VEC) supports real-time vehicular application services. However, accurate prediction and caching of popular content in roadside units (RSUs) while safeguarding user privacy is challenging. This study proposes a privacy-preserving cooperative hierarchical caching approach based on federated deep reinforcement learning (PCFR) for VEC. An asynchronous federated learning algorithm based on improved differential privacy is proposed, which considers the vehicle locations and movement directions and reasonably limits the local-update norm, improving the global model prediction accuracy while protecting user privacy. To address spatiotemporal variations in content popularity, a proposed attention-weighted asynchronous actor-critic collaborative caching algorithm extracts and weights key state features to optimize the collaborative cache content and its distribution location, enhancing the overall caching efficiency. In simulation, the PCFR scheme outperforms other caching schemes. With a 400-MB cache capacity, the PCFR scheme improves the cache hit rate by approximately 50.0% and reduces the content access delay by approximately 28.0%.

关键词： vehicular edge computing Cooperative caching Privacy preservation Asynchronous federated learning Differential privacy Deep reinforcement learning

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Efficient Multi-User Resource Allocation for Urban vehicular edge computing: A Hybrid Architecture Matching Approach

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IEEE TRANSACTIONS ON vehicular TECHNOLOGY 2025年第1期74卷 1811-1816页

作者： Xie, Hongyang Liu, Haoqiang Chen, Huiming Feng, Shaohan Wei, Zhaobin Zeng, Yonghong Southeast Univ Sch Artificial Intelligence Nanjing 211102 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 314423 Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610017 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore

Advanced in the proliferation of the Internet of Things (IoT), a plethora of functions have been integrated in vehicular networks and thereby transfered it into a smart network. However, the contradiction between the limited on-vehicle computing resource and the massive data collected by these IoT devices hinders the broader adoption of vehicular network as a vast variety of on-vehicle applications are latency-sensitive. To address this issue, vehicular edge computing has become a promising technology as it can offload a large number of tasks from its proximal vehicles. However, the offloading methods recently utilized are inefficient while dealing with multi-user vehicular networks under dynamic scenarios. To design a superior offloading method that can effectively and efficiently offload tasks from vehicles to servers, multiple objectives and constraints with various topologies should be considered. In this paper, instead of constructing a typical multi-user and multi-server vehicular edge computing scenario, a complex scenario with more uncertainties, i.e. urban scenario, is modeled. We propose a Hybrid Architecture Matching Algorithm (HAMA) to minimize the average time latency subject to the constraint on energy consumption and evaluate the proposed algorithm in the above two scenarios. Moreover, HAMA is constructed based on hybrid centralized-distributed architecture, which can process the centralized collected information on a distributed manner. Experimental results demonstrate that the matching algorithm can significantly reduce average time latency, achieving up to a 68% improvement compared to local execution.

关键词： Servers Delays Computational modeling Vehicle dynamics Urban areas Roads Handover vehicular edge computing matching algorithm resource allocation optimization algorithm

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