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基于遗传特征的车载网络分簇路由算法研究 被引量:1

Research on Clustering Routing Algorithm in Vehicle Network Based on Genetic Features
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摘要 路由算法是车载自组织网络的通信基础。然而现有的路由算法存在高时延、通信性能不稳定等缺陷,难以适应车辆变道预警、超车预警、碰撞预警和车载网络安全预警需求。基于此,文中采用IEEE802.11p通信标准基于经典的曼哈顿街区提出了基于遗传特征的分簇路由(genetic-characteristics-based clustering routing,GCCR)算法。该算法在分簇算法基础上,采取选择、交叉、变异操作对服务节点进行筛选,利用遗传算法自适应、择优等特性对分簇路由算法进行优化,既达到对服务节点优化的目的,又防止算法陷入局部最优。实验使用NS2软件仿真,并与经典AODV贪婪路由算法和LEACH分簇路由算法进行性能比较。实验结果表明,提出的基于遗传特征的分簇路由算法在数据包投递率、传输时延、网络开销方面具有明显的优势,符合车载网络安全预警应用的要求。 Routing algorithm is the communication foundation of vehicle network.However,the existing routing algorithms have defects such as high delay and unstable communication performance,which are difficult to adapt to the requirements of lane change warning,overtaking warning,collision warning and on-board network security warning.Based on this,we adopt IEEE802.11p communication standard and propose a genetic based clustering routing(GCCR)algorithm based on classic Manhattan Block.Based on the clustering algorithm,the service nodes are selected by selection,crossover and mutation,and the clustering routing algorithm is optimized by using the adaptive and selective features of genetic algorithm,which not only achieves the purpose of optimizing the service nodes,but also prevents the algorithm from falling into the local optimal.NS2 software is used in the experiment,and the performance is compared with the classical AODV greedy routing algorithm and LEACH clustering routing algorithm.The experiment shows that the clustering routing algorithm based on genetic characteristics proposed has obvious advantages in packet delivery rate,transmission delay and network overhead,and meets the requirements of on-board network security warning application.
作者 路婷 王伟 LU Ting;WANG Wei(School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China)
出处 《计算机技术与发展》 2021年第9期13-18,共6页 Computer Technology and Development
基金 国家自然科学基金项目(61902303)。
关键词 车载自组织网络 安全预警 分簇路由算法 遗传算法 NS2.35 vehicle ad hoc network security warning clustering routing algorithm genetic algorithm NS2.35
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