IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization

Abstract

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.

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