Red Deer Optimization Algorithm-Based RSUs Deployment Method for Maximizing Network Coverage in VANETs

In the intelligent transportation systems (ITS), roadside units (RSUs) need to be deployed strategically in vehicular ad hoc network (VANETs) for facilitating a pivotal entity which helps in potential interactions among vehicles. A well deployed RSUs methodology is determined to improved the quality of service since it helps in minimizing the financial costs with low deployment costs and maximized range of transmission. In this paper, Red Deer Optimization Algorithm-based RSUs deployment method is proposed as a adaptive strategy which helped in maximizing the network coverage in VANETs with flexible traffic demands. This RDOARDS approach considered the requirements of service and economic feasibility for improving the characteristics of traffic flow, road network topology and information coverage. It employed the merits of Red Deer Optimization Algorithm (RDOA) for the objective of determining the optimal points in the network over which the RSUs need to be positioned for maximized network coverage in VANETs. The results of this RDOARDS approach confirmed better coverage rate and minimized RSUs deployments compared to the baseline approached for the objective of improving the service quality and boosting network connectivity.