基于分簇与改进Q学习的车联网V2V复合路由算法  

V2V Composite Routing Algorithm for Internet of Vehicles Based on Clustering and Improved Q-Learning

作　　者：毕翔[1,2] 黄晃 张本宏[1,2] 卫星[1,2,3] BI Xiang;HUANG Huang;ZHANG Benhong;WEI Xing(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China;Engineering Research Center of Safety Critical Industrial Measurement and Control Technology,Ministry of Education,Hefei University of Technology,Hefei 230009,China;Intelligent Manufacturing Technology Research Institute,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009 [2]合肥工业大学安全关键工业测控技术教育部工程研究中心,合肥230009 [3]合肥工业大学智能制造技术研究院,合肥230009

出　　处：《计算机工程》2023年第3期221-230,247,共11页Computer Engineering

基　　金：安徽省博士后研究人员科研活动项目(2020B455);安徽省自然科学基金联合基金项目(2008085UD08);安徽省重点研发计划项目(202004a05020004)。

摘　　要：针对现有车联网路由算法存在路由维护开销大、动态适应性差的问题,提出一种基于改进Q学习的多跳分簇复合路由算法。在簇维护阶段,簇头根据网关效用性函数选择边缘车辆中通信性能较优且速度相对稳定的节点作为网关节点。在路由建立阶段,通过考虑链路通信质量、数据包传输方向和节点移动性三个方面,设计节点性能评估函数,用于评估所选择下一跳节点的综合性能,以避免出现“盲路”问题,在Q学习阶段,通过定量化方法表示相邻节点的链路持续时间和距离,并将其作为学习率和折扣率,以提升Q学习的学习效率。在德国科隆和国内某市移动数据集上的实验仿真结果表明,相比RSAR、GPSR和TCRA路由算法,该算法的路由生存时间、吞吐量平均提高17.71%和32.56%,通信延迟和丢包率平均降低14.3%和66.32%,能适应复杂多变的车辆自组织网络。Targeting the high maintenance costs and poor dynamic adaptability of existing routing algorithms for the Internet of vehicles,this study proposes a multi-hop clustering composite routing algorithm based on improved Qlearning.In the cluster maintenance phase,the cluster head selects the node with good communication performance and relatively stable speed with the edge vehicles as the gateway node according to the utility function of the gateway.In the route establishment stage,by considering the link communication quality,packet transmission direction,and node mobility,a node performance evaluation function evaluates the comprehensive performance of the selected next-hop node to avoid the“blind path”problem.In the Q-learning stage,the link duration and distance of adjacent nodes are expressed by a quantitative method and used as the learning and discount rates to improve the learning efficiency of Qlearning.Simulations on mobile datasets on Cologne,Germany and a city in China show that,compared with the RSAR,GPSR,and TCRA routing algorithms,the routing lifetime and throughput of the proposed algorithm improved by 17.71%and 32.56%on average,respectively,and the communication delay and packet loss rate decreased by 14.3%and 66.32%on average,respectively.These results indicate that the proposed algorithm can adapt to complex and Vehicular Ad Hoc Networks(VANETs).

关 键 词：车辆自组织网络 分簇机制 网关选择 复合路由 Q学习 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]