Honey Badger Optimization Algorithm-Based RSU Deployment for Improving Network Coverage in VANETs

Abstract

Road Side Units (RSUs) installed in roadside, and intersections in Vehicular Ad hoc NETwork (VANET) play an anchor role in aggregating and exploring intelligent data associated with vehicle traffic. These RSUs help in exchanging information among vehicles and obtaining early warning messages to ensure safety driving of vehicles. However, determining the number of RSUs and position over which they must be deployed are vital due to high cost incurred in implementing and maintaining them in the network. This problem of determining the number of RSUs along with their positions of deployment is a multi-objective problem, since it necessitates maximized coverage of network with minimized number of RSUs in the network. In this paper, Honey Badger Optimization Algorithm-based RSU Deployment (HBOA-RSUD) scheme is proposed with a multi-objective fitness function for improving network coverage in VANETs. This HBOA-RSUD initially establishes a static model for determining the complexity involved during the deployment of RSUs in the urban road. Then, a multi-objective HBOA algorithm with sigmoid function is applied over individual discrete values of fitness for identifying the position of RSU deployment. It determines the new positions of RSUs for enhancing the performance of the population and convergence speed. Experimental results of HBOA-RSUD confirm a maximized throughput by 21.38%, maximized network coverage by 28.95%, minimized delay by 19.42% and reduced energy consumption by 21.98% for varying number of RSUs in contrast to the existing intelligent RSU deployment approaches.