Treffer: Research on Edge Computing Offloading Strategy in Internet of Vehicles Based on Enhanced Black-winged Kite Algorithm.

The demand for task offloading in the Internet of Vehicles (IoV) has increased significantly in recent years. The growing computational demands of advanced applications pose a significant challenge to the limited processing capacity of onboard hardware. To address the limitations caused by insufficient computational resources in the Internet of Vehicles (IoV), this study proposes a cooperative task offloading scheme within mobile edge computing (MEC) that utilizes the processing power of parked vehicles. The strategy involves a task offloading model that takes into account task execution delay, computing energy consumption, and the resource constraints of both edge servers and idle vehicles. To handle the system utility optimization problem subject to constraints, this study introduces an Enhanced Black-winged Kite Algorithm (EBKA) designed for improved solution efficiency. First, this algorithm incorporates an elite opposition-based learning strategy during initialization phase. Second, it integrates the Gompertz function in the attack behavior phase. Third, a Gaussian mutation strategy is applied during the migration behavior phase. These modifications collectively accelerate convergence speed and improve accuracy. According to the simulation analysis, the proposed offloading strategy yields notable improvements by reducing both system delay and overall energy usage during task execution. [ABSTRACT FROM AUTHOR]