Energy-Efficient Computation Offloading Scheme for Vehicular Edge Computing Using Improved Golden Eagle Optimization Algorithm

The increasing processing demands of vehicle applications pose a major challenge for the Internet of Vehicles (IoVs). Multi-access edge computing (MEC) offloads computation-intensive activities to edge servers, significantly enhancing computing capacity and extending vehicle battery life. Nevertheless, traditional offloading strategies fail to effectively balance computation tasks among vehicles and edge servers, resulting in suboptimal resource utilization and increased energy consumption. Therefore, an Energy-Efficient Computation Offloading Scheme for Vehicular Edge Computing utilizing Improved Golden Eagle Optimization Algorithm (EECO-VEC-IGEOA) is proposed. An Improved Golden Eagle Optimization Algorithm (IGEOA) is designed to optimize energy-efficient computation offloading in vehicular edge computing networks (VECN). By dynamically allocating computation tasks between vehicles and edge servers depending on real-time conditions, the EECO-VEC-IGEOA model aims to improve overall network performance and energy efficiency. The EECO-VEC-IGEOA method reduces energy consumption by 14.62%, 16.84%, and 19.16%, and task completion time by 15.84%, 18.92%, and 20.69% compared to the existing approaches.