Joint resource allocation and task offloading for latency constraint and energy-sensing vehicular edge computing

In the Internet of vehicles, the emergence of computationally intensive and latency-sensitive vehicular applications poses great challenges for computing time and power consumption. Vehicle edge computing (VEC) is therefore proposed to take advantages of the computing capabilities of vehicles and roadside unit (RSU). However, the efficient resource management for VEC systems to achieve optimised performance remains unsolved. Based on this, the issue of collaborative resource allocation and task offloading in the VEC system was studied, with partial tasks being offloaded through vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. At the same time, the selection decisions, task offloading ratios, and the allocation of computing resources among vehicles and RSUs were collaboratively optimized, with the aim of minimizing the total system energy consumption while ensuring task completion. As the proble was a NP-hard mixed-integer nonlinear programming problem, the optimization problem was decoupled into multiple subproblems, and subsequently solved by using ant colony system (ACS), Lagrange multipliers and gradient method. Extensive simulation results demonstrate that the proposed scheme significantly outperforms the benchmarking algorithms in terms of task incomplete rate and overall system energy consumption.