TY -的A2 -李,Sungchang盟,塔里克Rehan AU -伊克巴尔Zeshan盟——Aadil Farhan PY - 2020 DA - 2020/11/07 TI - IMOC:优化技术Drone-Assisted VANET (DAV)基于蛾火焰优化SP - 8860646六世- 2020 AB -技术进步领域的车载ad hoc网络(VANETs)提高智能交通以及许多其他应用程序。路由在VANETs很难相比移动ad hoc网络(manet);拓扑约束,如高迁移率、节点密度和频繁路径失败使VANET路由更具挑战性。规模复杂的路由问题,静态和动态工艺路线不工作,介绍了基于ai集群技术。进化算法聚类技术用于解决路由问题;蛾火焰优化就是其中之一。在这项工作中,一个智能蛾火焰文中针对集群(IMOC) drone-assisted车载网络提出了。这种技术是用来最大限度地覆盖了最低的车辆节点集群头(CHs)所需的路由。交付系统开销最小的最优路线,提供端到端的连接,是本文解决的核心问题。节点密度、网格大小和传输范围使用的性能指标进行比较分析。 These parameters were varied during simulations for each algorithm, and the results were recorded. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid. SN - 1530-8669 UR - https://doi.org/10.1155/2020/8860646 DO - 10.1155/2020/8860646 JF - Wireless Communications and Mobile Computing PB - Hindawi KW - ER -