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Unlicensed assisted transmission in vehicular edge computing networks

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022年第1期2022卷 1-20页

作者： Ji, Zhongbao Lu, Xiao Yin, Rui Wu, Celimuge Wenzhou Polytech Sch Intelligent Mfg Wenzhou Peoples R China Zhejiang Univ Sch Informat & Elect Engn Hangzhou Peoples R China Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou Peoples R China Univ Electrocommun Grad Sch Informat & Engn Tokyo Japan

Recently, the emerging computation-intensive in-vehicle applications and the exponentially growing data have posed a serious challenge to resource-limited in-vehicle devices. By integrating the mobile edge computing (MEC) into the vehicle networks, vehicular edge computing (VEC) is envisioned as a promising solution. In this paper, we divide the VEC system into small time slots, which effectively simulates dense small-scale computing scenarios in the rapidly varying channel environment over time. Furthermore, to alleviate the shortage of spectrum resources, vehicles can offload part of computing tasks to the edge server not only through licensed channels but also through unlicensed channels. However, dynamic topology changes and complex communication characteristics of the vehicle networks lead to the strategies of task offloading and resource allocation being difficult to obtain. By decoupling the non-convex optimizing problem and splitting it into multiple sub-problems, we design a joint power, spectrum and computing resource allocation strategy with low complexity, in order to minimize the total energy consumption of all vehicles for processing tasks. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme that employing licensed and unlicensed access in VEC networks can reduce the energy consumption of vehicles for processing tasks in comparison with baseline schemes.

关键词： Mobile edge computing vehicular edge computing Task offloading Resource allocation Unlicensed channels

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Computation Resource Configuration With Adaptive QoS Requirements for vehicular edge computing: A Fluid-Model Based Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 21148-21162页

作者： Zhu, Kun Zhou, Yue Yi, Changyan Wang, Ran Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 211106 Jiangsu Peoples R China

In this paper, the computation resource configuration for vehicular edge computing is investigated in this study. Dissimilar to a large portion of the current literature, we center around the problem of determining the optimal edge computing resource allocation to vehicles with computation offloading requests for maximizing the long-term management profit of the network operator (i.e., the road-side unit of the vehicular network) under the randomness of vehicular traffics and task processing. A multi-type management framework is used to characterize the heterogeneities among different vehicles in terms of their edge computing quality-of-service (QoS) requirements. A novel fluid model is proposed that facilitates the formulation of the corresponding resource optimization problem by taking into account the system's steady state characteristics with dynamic evolutions. In addition, rather than considering fixed QoS requirements in long-run, we explore the impact of the resulted service quality on the QoS requirements determined by vehicles. The QoS requirements of each vehicle is allowed to change adaptively according to the service quality fed back by the system. Based on this, we propose a simple but efficient approach, called threshold-based computation resource configuration scheme (TCRCS). The proposed solution's performance is assessed by theoretical analysis and simulations, which show that it outperforms competitors.

关键词： edge computing Quality of service Task analysis Servers Resource management Computational modeling Cloud computing vehicular edge computing resource allocation fluid model adaptive QoS requirements

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Mobility-Aware Multi-Hop Task Offloading for Autonomous Driving in vehicular edge computing and Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第2期24卷 2169-2182页

作者： Liu, Lei Zhao, Ming Yu, Miao Jan, Mian Ahmad Lan, Dapeng Taherkordi, Amirhosein Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Guangzhou Inst Technol Guangzhou 510555 Peoples R China Qingdao Univ Coll Text & Clothing Qingdao 266071 Peoples R China Abdul Wall Khan Univ Mardan Dept Comp Sci Mardan 23200 Pakistan Univ Oslo Dept Informat N-0373 Oslo Norway

vehicular edge computing (VEC) has gained increasing interest due to its potential to provide low latency and reduce the load in backhaul networks. In order to meet drastically increasing computation demands from emerging ever-growing vehicular applications, e.g., autonomous driving, abundant computation resources of individual vehicles can play a crucial role in task execution in a VEC scenario, that can further contribute in considerably improving user experience. This is however an extremely challenging task due to high mobility of vehicles that can easily lead to intermittent connectivity, thereby disrupting on-going task processing. In this paper, we propose a task offloading scheme by exploiting multi-hop vehicle computation resources in VEC based on mobility analysis of vehicles. In addition to the vehicles within one hop from the task vehicle that generates computation tasks, certain multi-hop vehicles that meet the given requirements in terms of link connectivity and computation capacity, are also leveraged to carry out the tasks offloaded by the task vehicle. An optimization problem is formulated for the task vehicle to minimize the weighted sum of execution time and computation cost of all tasks. A semidefinite relaxation approach with an adaptive adjustment procedure is proposed to solve the formulated optimization problem for obtaining the corresponding offloading decisions. The simulation results show that our proposed offloading scheme can achieve significant improvement in terms of response delay by at least 34% compared with the other algorithms (e.g., local processing and random offloading).

关键词： Task analysis Servers Resource management Delays Optimization Spread spectrum communication Electronic mail vehicular edge computing task offloading multi-hop mobility connectivity

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Joint Secure Offloading and Resource Allocation for vehicular edge computing Network: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第5期24卷 5555-5569页

作者： Ju, Ying Chen, Yuchao Cao, Zhiwei Liu, Lei Pei, Qingqi Xiao, Ming Ota, Kaoru Dong, Mianxiong Leung, Victor C. M. Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Royal Inst Technol KTH Dept Informat Sci & Engn S-11428 Stockholm Sweden Muroran Inst Technol Dept Informat & Elect Engn Muroran 0500071 Japan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner's wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

关键词： Resource management Vehicle dynamics Task analysis Heuristic algorithms Dynamic scheduling Security Eavesdropping vehicular edge computing physical layer security spectrum sharing multi-user cooperative offloading deep reinforcement learning

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Meta Reinforcement Learning for Multi-Task Offloading in vehicular edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2024年第3期23卷 2123-2138页

作者： Dai, Penglin Huang, Yaorong Hu, Kaiwen Wu, Xiao Xing, Huanlai Yu, Zhaofei Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transpo Chengdu 611756 Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan 063000 Peoples R China Peking Univ Inst Artificial Intelligence Beijing 100871 Peoples R China

Mobile edge computing has been a promising solution to enable real-time service in vehicular networks. However, due to high dynamics of mobile environment and heterogeneous features of vehicular services, traditional expert-based or learning-based strategies has to update handcrafted parameters or retrain learning model, which leads to intolerant overhead. Therefore, this paper investigates the problem of multi-task offloading (MTO), where there exist multiple offloading scenarios with varying parameters, such as task topology, resource requirement and transmission/computation capability. The objective is to design a unified solution to minimize task execution time under different MTO scenarios. Accordingly, we develop a Seq2seq-based Meta Reinforcement Learning algorithm for MTO (SMRL-MTO). Specifically, a bidirectional gated recurrent units integrated with attention mechanism is designed to determine offloading action by encoding sequential offloading actions and showing different preferences to different parts of input sequence. Particularly, a meta reinforcement learning framework is designed based on model-agnostic meta learning, which trains a meta policy offline and fast adapts to new MTO scenario within a few training steps. Finally, we conduct performance evaluation based on task generator DAGGEN and realistic vehicular traces, which shows that the SMRL-MTO reduces task execution time by 11.36% on average compared with greedy algorithm.

关键词： Task analysis Servers Heuristic algorithms Computational modeling Computer architecture Delays Vehicle dynamics Meta reinforcement learning multi-task offloading vehicular edge computing multi-task dependency Seq2seq model

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Joint Road Side Units Selection and Resource Allocation in vehicular edge computing

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IEEE TRANSACTIONS ON vehicular TECHNOLOGY 2021年第12期70卷 13190-13204页

作者： Li, Shichao Zhang, Ning Chen, Hongbin Lin, Siyu Dobre, Octavia A. Wang, Haitao Guilin Univ Elect Technol Guilin 541004 Peoples R China Univ Windsor Windsor ON N9B 3P4 Canada Beijing Jiaotong Univ Beijing 100044 Peoples R China Memorial Univ St John NF A1B 3X9 Canada

With powerful storage and computation capability, vehicular edge computing is considered as a promising paradigm to enhance the safety and quality-of-service of vehicles in intelligent transportation systems. In this work, we investigate a joint road side units (RSUs) selection and resource allocation problem, with the objective of minimizing the total task offloading delay subject to the bandwidth and computation resources constraints, in non-collaborative and collaborative RSU scenarios. For the former, as the formulated problem is a mixed-integer nonlinear programming problem, we re-formulate the original problem into a convex one and then decompose it into a distributed manner. By utilizing the alternating direction method of multipliers, we propose a joint RSUs selection and resource allocation (JRSRA) algorithm. For the collaborative RSU scenarios, we transform the problem into a convex optimization problem by linearization, and propose a joint resource allocation based branch and bound (JRABB) algorithm to solve the total task offloading delay minimization problem. Simulation results show that the proposed JRSRA and JRABB algorithms can reduce the total task offloading delay compared with other benchmark methods.

关键词： Task analysis Delays Resource management Servers Computational modeling Collaboration Data models vehicular edge computing joint road side units selection and resource allocation non-collaborative and collaborative RSU scenarios alternating direction method of multipliers branch and bound

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Enhancing vehicular edge computing system through cooperative computation offloading

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第1期26卷 771-788页

作者： Lu, Yanfei Han, Dengyu Wang, Xiaoxuan Gao, Qinghe Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

vehicular edge computing is the specific application of Mobile edge computing in vehicular scenarios. In this work, to efficiently provide on-demand computational resource access for Mission Vehicles, Road Side Units (RSUs) and Cooperative Vehicles are both mounted with edge servers. To further improve the performance of this system, we firstly study the dynamic and cooperative task caching between RSUs. Under the control of the central controller, our caching scheme fully utilizes edge servers' storage resources. Then, we resort to Deep Deterministic Policy Gradient (DDPG) algorithm to adapt to the time-varying wireless environment. Moreover, we propose the DDPG-OCWB algorithm to improve the convergence performance of DDPG. Finally, numerical results verify the improved performance of our proposed algorithm. Based on a detailed comparison with existing research schemes, it is evident that our scheme dramatically reduces system delay while utilizing edge servers' energy efficiently.

关键词： Computation offloading Task caching Workload balancing vehicular edge computing

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Joint Service Caching and Computation Offloading Scheme Based on Deep Reinforcement Learning in vehicular edge computing Systems

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IEEE TRANSACTIONS ON vehicular TECHNOLOGY 2023年第5期72卷 6709-6722页

作者： Xue, Zheng Liu, Chang Liao, Canliang Han, Guojun Sheng, Zhengguo Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Univ Sussex Dept Engn & Design Brighton BN19RH England

vehicular edge computing (VEC) is a new computing paradigm that enhances vehicular performance by introducing both computation offloading and service caching, to resource-constrained vehicles and ubiquitous edge servers. Recent developments of autonomous vehicles enable a variety of applications that demand high computing resources and low latency, such as automatic driving, auto navigation, etc. However, the highly dynamic topology of vehicular networks and limited caching space at resource-constrained edge servers calls for intelligent design of caching placement and computation offloading. Meanwhile, service caching decisions are highly correlated to the computation offloading decisions, which pose a great challenge to effectively design service caching and computation offloading strategies. In this paper, we investigate a joint optimization problem by integrating service caching and computation offloading in a general VEC scenario with time-varying task requests. To minimize the average task processing delay, we formulate the problem using long-term mixed integer non-linear programming (MINLP) and propose an algorithm based on deep reinforcement learning to obtain a suboptimal solution with low computation complexity. The simulation results demonstrate that our proposed scheme exhibits an effective performance improvement in task processing delay compared with other representative benchmark methods.

关键词： Task analysis Servers Delays Vehicle dynamics Optimization edge computing Resource management vehicular edge computing service caching computation offloading deep reinforcement learning

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Digital twin-assisted resource allocation framework based on edge collaboration for vehicular edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 150卷 243-254页

作者： Jeremiah, Sekione Reward Yang, Laurence Tianruo Park, Jong Hyuk Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn 232 Gongneung Ro Seoul 01811 South Korea Huazhong Univ Sci & Technol Sch Comp Sci & Technol 1037 Luoyu Rd Wuhan Hubei Peoples R China Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn 232 Gongneung Ro Seoul 01811 South Korea

vehicular edge computing (VEC) supports latency-sensitive and computation-intensive vehicular applications by providing caching and computing services in vehicle proximity. This reduces congestion and transmission latency. However, VEC faces implementation challenges due to high vehicle mobility and unpredictable network dynamics. These challenges pose difficulties to network resource allocation. Most existing VEC network resource management solutions consider edge-cloud collaboration and ignore collaborative computing between edge nodes. A reasonable collaboration between Roadside Units (RSUs) or small cells eNodeB can improve VEC network performance. Our proposed framework aims to improve VEC network performance by integrating Digital Twin (DT) technology which creates virtual replicas of network nodes to estimate, predict, and evaluate their real-time conditions. A DT is constructed centrally to maintain and simulate VEC network, thus enabling edge nodes collaboration and real-time resources information availability. We employ channel state information (CSI) for RSUs selection, and vehicles communicate with RSUs through a non-orthogonal multiple access (NOMA) protocol. We aim to maximize the VEC system computation rate and minimize task completion delay by jointly optimizing offloading decisions, subchannel allocation, and RSU association. In view of the resulting optimization problem complexity (NP-hard), we model it as a Markov Decision Process (MDP) and apply Advantage Actor-Critic (A2C) algorithm to solve it. Validated via simulations, our scheme shows superiority to the benchmarks in reducing task completion delay and improving VEC system computation rates.(c) 2023 Published by Elsevier B.V.

关键词： Digital twin edge cooperation Resource allocation Artificial intelligence vehicular edge computing Deep reinforcement learning

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Adaptive Digital Twin for vehicular edge computing and Networks

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Journal of Communications and Information Networks 2022年第1期7卷 48-59页

作者： Yueyue Dai Yan Zhang School of Cyber Science and Engineering and Research Center of 6G Mobile Communications Huazhong University of Science and TechnologyWuhan 430000China Department of Informatics University of Oslo0316 OsloNorway

To better support the emerging vehicular applications and multimedia services,vehicular edge computing(VEC)provides computing and caching services in proximity to vehicles, by reducing network transmission latency and alleviating network congestion. However, current VEC networks may face some implementation challenges, such as high mobility of vehicles, dynamic vehicular environment,and complex network scheduling. Digital twin, as an emerging technology, can make the virtual representation of physical networks to predict, estimate,and analyze the real-time network state. In this paper, we integrate digital twin into VEC networks to adaptively make network management and policy schedule. We first introduce the framework of VEC networks and present the key problems in a VEC ***,we give the concept of digital twin and propose an adaptive digital twin-enabled VEC network. In the proposed network, digital twin can enable adaptive network management via the two-closed loops between physical VEC networks and digital twins. Further,we propose a digital twin empowered VEC offloading problem with vehicle digital models and road side unit (RSU) digital models. A deep reinforcement learning (DRL)-based offloading scheme is designed to minimize the total offloading latency. Numerical results demonstrate the effectiveness of the proposed DRL-based algorithm for VEC offloading.

关键词： adaptive digital twin vehicular edge computing deep reinforcement learning

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