Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs

doi:10.1109/TITS.2021.3109154

	Load Balance Guaranteed Vehicle-to-Vehicle Computation Offloading for Min-Max Fairness in VANETs
	Wu, Yalan 1; Wu, Jigang 1; Chen, Long 1; Yan, Jiaquan 1; Han, Yinhe 2
	2021-09-08
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
页码	20
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.
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
DOI	10.1109/TITS.2021.3109154
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000732418800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17987
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.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, 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.