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

Rehan Tariq,Farhan Aadil,Zeshan Iqbal,Sungchang Lee

doi:10.1155/2020/8860646

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.

Highlights

Vehicular ad hoc networks (VANETs) are different from mobile ad hoc networks (MANETs); clustering algorithms designed for MANETs cannot be applied to VANETs
We proposed an intelligent moth flame clustering optimization for VANET (IMOC) to optimize the clustering problem in VANETs with air assistance of FANETs
To solve the VANET routing problem, intelligent moth flame optimization-based clustering (IMOC) solution presented an evolutionary algorithm based on cluster optimization

Summary

Introduction

Vehicular ad hoc networks (VANETs) are different from mobile ad hoc networks (MANETs); clustering algorithms designed for MANETs cannot be applied to VANETs. In traditional VANETs, infrastructure, like roadside units (RSUs), is used to provide network services to vehicular nodes, selecting the optimal paths and transmitting data This infrastructure provides road safety information, road congestion, alternative routes, along with weather conditions to drivers. The addition of UAVs in existing VANETs is a challenging task because they have very distinct features as compared with ground nodes/vehicle Another challenge is the efficient utilization of flight time of UAVs because UAVs carry limited energy resources [4]. If the route is lost, the packet takes a lot of time to reach a destination with higher travelling cost To solve these issues, FANET assistance will provide a better solution to solve irregularities in traditional VANETs. Genetic algorithms/programming, evolutionary strategies, and learning classifier systems are some types evolutionary algorithms [7, 8]. Once artificial light comes across a straight path that is being followed, moths try to keep the angle toward the

Broadcasted position of each node in the search space

37. Best solution from search is equal to total number of

IMOC-Proposed Methodology

Experimental Results and Analysis

Results and Discussion

Conclusion