Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles

An edge computing offloading strategy was proposed with the goal of addressing issue low efficiency and service quality in multi-service multi-user intersections networked vehicles. This took into account all relevant factors, including matching users nodes, ratio, bandwidth power resource allocation, system energy consumption. It is mainly divided 2 tasks: (1) Service node selection: A fuzzy logic-based selection algorithm (SNFLC) proposed. The linear equation for performance value determined through reasoning by specifying three indexes as input. Gradient descent method used to find optimal objective function, Lyapunov criterion coefficient introduced improve stability algorithm. (2) Offload ratio allocation are solved: coupling between offload confirmed relaxing integer variables because optimization problem a NP problem, two sub-problems. At same time, low-complexity alternate iteration (LC-IRA) solve computational allocation. According simulation results, genetic ant colony (G_ACA), non orthogonal multiple access technology (NOMA) LC-IRA improved 26.5%, 31.37%, 45.52%, respectively, compared random unloading (RUA) average distribution (AD).