Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs
文献类型：期刊论文
| 作者 | Wu, Yalan2; Wu, Jigang2; Chen, Long2; Yan, Jiaquan2; Han, Yinhe1 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2021-09-08 |
| 页码 | 20 |
| 关键词 | Task analysis Vehicular ad hoc networks Delays Heuristic algorithms Power control Energy consumption Reinforcement learning VANET task offloading load balance power control machine learning |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2021.3109154 |
| 英文摘要 | Load balance in vehicular ad hoc networks (VANETs) is a challenge in vehicle-to-vehicle computation offloading, due to stochastic requests of users, heterogeneous service capabilities and high mobility of vehicles, etc. This paper aims to fill this gap by formulating a problem for load balance in a VANET, with the objective of minimizing the maximum load under transmit power, storage capacity, per task completion time and energy consumption constraints. The formulated problem is proved to be NP-hard, then it is investigated by decomposing it into two subproblems, i.e., how to offload tasks for the case of fixed transmit power and how to adjust transmit power for the given offloading decision. For the first subproblem, an approximation algorithm is proposed by offloading the tasks in the vehicle with the maximum load to the vehicle with minimum load. Meanwhile, a deep reinforcement learning algorithm is proposed, in order to focus on the network dynamics. A coalition based algorithm, a distributed coalition based algorithm, as well as an incentive algorithm based on deep reinforcement learning, are proposed to maximize the total payoff for the selfishness of vehicles. For the second subproblem, an adjustment strategy for transmit power is customized to further reduce the computing load. The algorithms are evaluated on an integrated simulation platform with open street map, SUMO, NS-3 and dataset of Google cluster-usage traces. Simulation results show that, the proposed algorithms outperform three state-of-the-art works for most cases, in terms of the maximum load. The proposed distributed algorithm can significantly accelerate the proposed centralized algorithm with acceptable increase in maximum load. Besides, the load can be further reduced by the proposed adjustment strategy. |
| 资助项目 | National Key Research and Development Program of China[2018YFB1003201] ; National Natural Science Foundation of China[62072118] ; National Natural Science Foundation of China[62106052] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000732418800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17986]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wu, Jigang; Chen, Long |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Yalan,Wu, Jigang,Chen, Long,et al. Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021:20. |
| APA | Wu, Yalan,Wu, Jigang,Chen, Long,Yan, Jiaquan,&Han, Yinhe.(2021).Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,20. |
| MLA | Wu, Yalan,et al."Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021):20. |
入库方式： OAI收割
来源：计算技术研究所
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