LEO Satellite-Assisted Vehicular Edge Computing

In the Internet of Vehicles (IoV) applications, vehicle and in-vehicle users need to process computation-intensive and latency-sensitive tasks. Offloading tasks to satellite edge servers can alleviate the computational burden and reduce vehicular users' energy consumption, especially in remote areas. In this paper, we study a low earth orbit (LEO) satellite-assisted vehicular edge computing system. We jointly consider user association variable, task allocation, power control, and bandwidth allocation to minimize weighted sum-energy consumption in the system. Specifically, we design an iterative optimization algorithm, which decomposes the original non-convex problem into several sub-problems. We solve each sub-problem efficiently using variable substitutions, and the Karush-Kuhn-Tucke (KKT) condition. Simulation results show that the proposed algorithm has a good convergence performance and effectively reduces system weighted sum-energy consumption compared to benchmarks.