RAMC: Reverse-Auction-Based Multilevel Cooperation for Large Size Data Download in VANETs

被引:1
作者
Wang, Shujuan [1 ]
Ma, Fangfei [1 ]
Yu, Zhengtao [2 ]
机构
[1] Kunming Univ Sci & Technol, Sch Informat Engn & Automation, Kunming 650500, Yunnan, Peoples R China
[2] Kunming Univ Sci & Technol, Key Lab Artificial Intelligence Yunnan Prov, Kunming 650500, Yunnan, Peoples R China
基金
中国国家自然科学基金;
关键词
Costs; Vehicle-to-everything; Reliability; Topology; Delays; Task analysis; Simulation; Cooperative data download; multilevel cooperation; reverse auction; V2X communication; vehicular ad hoc networks (VANETs); SCHEME;
D O I
10.1109/JIOT.2023.3329392
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The cooperative data download in vehicular ad hoc networks (VANETs) is attracting more and more attention since a great number of services and applications, which aim at improving user safety and promoting overall travel experience, are based on the efficient and reliable data download among vehicles. However, the characteristics of VANETs, such as fast-moving vehicles, quick-changing topologies, and unstable channel conditions, put great challenge on the realization of efficient cooperative data download, especially for large size data download in highway environment. In this article, we present an efficient and economical cooperative data download mechanism for large size data in highway environment. A multilevel cooperation strategy is designed to enable progressive cooperation and to allow continuous adding of new cooperative vehicles as needed, so that the resources of candidate cooperative vehicles can be utilized sufficiently. Moreover, a reverse auction-based vehicles selection method is proposed to work closely with the multilevel cooperation strategy, to find the most appropriate cooperative vehicles, which can accomplish the data download task with the lowest cost. In addition, the performance degradation introduced by the communication blind zones between road side units (RSUs) is resolved by a carefully designed data forwarding policy. The proposed mechanism focuses on helping vehicles download large size data not only efficiently but also economically, thus distinct it from existing works. Simulation results prove that the proposed mechanism achieves remarkable performance gain in terms of data download cost, download time, completion ratio and the amount of downloaded data in one download period.
引用
收藏
页码:11087 / 11100
页数:14
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