Improved NSGA-II algorithm-based task offloading decision in the internet of vehicles edge computing scenario

In the context of Mobile Edge Computing (MEC) for the Internet of Vehicles (IoV), vehicles can establish communication connections with other entities to access relevant IoV services. However, current research on offloading schemes rarely considers the utilization of idle computing resources available on the road. To address this issue and provide a more efficient offloading scheme that makes more efficient use of computing resources, we propose a vehicle-edge-cloud collaborative offloading scheme incorporating Vehicle-to-Vehicle (V2V) communication. Our scheme effectively utilizes the computing resources of Task-Requesting Vehicles (TRVs), Idle Computing Resource Vehicles (ICRVs), edge devices, and the cloud. First, a vehicle position determination mechanism is designed to ensure the stability of the task offloading process. Second, latency models, energy consumption models, task completion quality models, and multi-objective optimization models are constructed. In addition, an improved NSGA-II algorithm is proposed for task offloading decisions. Finally, the feasibility and stability of the proposed scheme are validated through simulation experiments. The results show that, compared with other schemes, the proposed scheme significantly improves system latency, energy consumption, and service quality.