Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization is one of them. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. 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.

中文翻译：

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IMOC：基于飞蛾火焰优化的无人机辅助VANET（DAV）优化技术

车载自组织网络（VANET）领域的技术进步改善了智能交通及其许多其他应用。与移动自组织网络（MANET）相比，VANET中的路由比较困难。高移动性，节点密度和频繁的路径故障等拓扑约束使VANET路由更具挑战性。为了解决静态和动态路由无法正常工作的复杂路由问题，引入了基于AI的群集技术。基于进化算法的聚类技术被用来解决这种路由问题。飞蛾火焰优化就是其中之一。在这项工作中，提出了一种用于无人机辅助车辆网络的基于智能飞蛾火焰优化的聚类（IMOC）。此技术用于为车辆节点提供最大的覆盖范围，并具有路由所需的最小簇头（CH）。通过以最小的开销提供端到端连接来提供最佳路由是本文解决的核心问题。节点密度，网格大小和传输范围是用于比较分析的性能指标。在每种算法的仿真过程中，这些参数都会发生变化，并记录结果。使用路由的最新集群算法进行了比较，例如蚁群优化（ACO），综合学习粒子群优化（CLPSO）和灰狼优化（GWO）。IMOC的实验结果始终优于每种情况的最新技术。还提出了一种在商用无人飞行器（UAV）的支持下通过最小化VANET中的路径创建开销来改进路由的框架。UAV对群集的支持通过保持相同网格中群集间通信的路由成本不变，从而改善了端到端连接性。