Joint Deployment Optimization of Fixed and Vehicle-Mounted Edge Servers for Urban Internet of Vehicles

In the Internet of Vehicles IoV , the user computation demand varies spatially and temporally. Thus, traditional static edge servers with fixed capacity at fixed sites lack the flexibility to handle such user dynamic. To this end, we study the joint deployment optimization of fixed and vehicle-mounted edge servers for an IoV system, where fixed servers FESes offer the basic coverage of the computation offloading service, and vehicle-mounted edge servers VESes focus on serving demand hotspots on the move. We first design the GICUNet traffic flow prediction model to precisely forecast the future traffic. Next, we allocate the computation capacity to each FES using Bayesian Optimization to minimize the deployment cost. We then design a Mobile Server scheduling algorithm based on Bipartite Graph Rematching MS-BGR to plan the short-distance paths of the VESes that cover most of the user demand. Experimental results show that our solution is excellent in terms of traffic prediction accuracy, adaptability to spatio-temporal dynamic user demand, and energy-efficiency of the VES travel paths compared with existing popular algorithms.