Dynamic Deployment of Mobile Roadside Units in Internet of Vehicles

Abstract

Mobile roadside units have crucial role in ensuring efficient communication, computing, and caching services in internet of vehicles (IoVs) for vehicles traversing urban landscapes. The dynamic nature of urban environments faces challenges in optimizing the deployment of mRSUs to adapt to varying vehicular densities and traffic patterns in real-time. In this article, we propose a novel real-time optimization approach for the dynamic deployment of mobile Roadside Units (mRSUs) in urban environments to support the rapid growth of the IoV. The proposed method is a novel allocation strategy based on Minimum Dominating Set (MDS) theory, which is demonstrated to significantly reduce the number of mRSUs required. This reduction is achieved without compromising the efficiency and effectiveness of the network, thereby ensuring rapid and reliable communication within the IoV. This approach addresses critical challenges in IoV deployment, particularly the limitations of vehicle computing resources and the need for high-bandwidth communication in order for smarter, more sustainable urban mobility solutions. Evaluation results show a a significant reduction in the number of minimum nodes, i.e. mRSUs, required for the IoV coverage by approximately 73%-96% of the original number of candidate nodes. Benchmarking consistently demonstrates the superiority of the proposed method over others, guaranteeing competitiveness and efficiency, with experimental results validating its reliability, real-time responsiveness, and ability to accommodate real-life constraints as needed.

Keywords

• Internet of Vehicles
• mobile roadside units
• heuristic search
• deep heuristic search
• metaheuristics
• deep adaptive memory programming