Multi-Objective Optimal Roadside Units Deployment in Urban Vehicular Networks

Abstract

The significance of transportation efficiency, safety, and related services continues to increase in urban vehicular networks. Within such networks, roadside units (RSUs) serve as intermediaries in facilitating communication. Therefore, the deployment of RSUs is of utmost importance in ensuring the quality of communication services. However, the optimization objectives, such as time delay and deployment cost, are commonly developed from diverse perspectives. As a result, it is possible that conflicts may arise among the objectives. Furthermore, in urban environments, the presence of various obstacles, such as buildings, gardens, lakes, and other infrastructure, poses challenges for the deployment of RSUs. Consequently, the deployment encounters significant difficulties due to the existence of multiple objectives, constraints imposed by obstacles, and the need to explore a large-scale optimization space. To address this issue, two versions of multi-objective optimization algorithms are proposed in this paper. By utilizing a multi-population strategy and an adaptive exploration technique, the proposed methods efficiently explore a large-scale decision-variable space. In order to mitigate the issue of an overcrowded deployment of RSUs, a calibrating mechanism is adopted to adjust RSU density during the optimization procedures. The proposed methods also address data offloading between vehicles and RSUs by setting up an iterative best response sequence game (IBRSG). Comparative analyses against several state-of-the-art algorithms demonstrate that our strategies achieve superior performance in both high-density and low-density urban scenarios. The results indicate that the proposed solutions significantly enhance the efficiency of vehicular networks.