Optimization Deployment of Roadside Units with Mobile Vehicle Data Analytics

Mobile self-organizing networks, such as vehicular ad-hoc network (VANET), in most cases rely on infrastructure deployment to provide access to internet services and other resource. Therefore, it is crucial to optimize the deployment of roadside units (RSUs) in vehicle network. In this paper, we propose a RSU optimized deployment scheme based on large vehicle data, which considers the deployment cost and latency performance synthetically. We deploy the RSU problem as a multiobjective optimization problem for mathematical modeling. On this basis, two-step solution is proposed: firstly, the RSU candidate positions can be obtained by considering the road topology and large vehicle data; secondly, the branch and bound algorithm is used to obtain the better RSU deployment based on the mathematical model. The simulation results show that the proposed RSU deployment scheme uses a small amount of RSU can achieve high coverage and greatly reduce the deployment cost. Moreover, the low latency performance of the vehicle access network and the quality of the latency sensitive application service can also be ensured.