计算机科学
强化学习
排队
车载自组网
服务质量
持续时间(音乐)
网络数据包
数据库事务
计算机网络
控制(管理)
质量(理念)
服务(商务)
智能交通系统
人工智能
无线自组网
电信
无线
运输工程
艺术
经济
哲学
程序设计语言
经济
工程类
文学类
认识论
作者
A. Zeynivand,A. Javadpour,S. Bolouki,A.K. Sangaiah,F. Ja’fari,P. Pinto,W. Zhang
标识
DOI:10.1016/j.jnca.2022.103497
摘要
One of the technologies based on information technology used today is the VANET network used for inter-road communication. Today, many developed countries use this technology to optimize travel times, queue lengths, number of vehicle stops, and overall traffic network efficiency. In this research, we investigate the critical and necessary factors to increase the quality of VANET networks. This paper focuses on increasing the quality of service using multi-agent learning methods. The innovation of this study is using artificial intelligence to improve the network’s quality of service, which uses a mechanism and algorithm to find the optimal behavior of agents in the VANET. The result indicates that the proposed method is more optimal in the evaluation criteria of packet delivery ratio (PDR), transaction success rate, phase duration, and queue length than the previous ones. According to the evaluation criteria, TSR 6.342%, PDR 9.105%, QL 7.143%, and PD 6.783% are more efficient than previous works.
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