Abstract:

With the extensive development of intelligent transportation and eco-friendly travel, a low-energy task-offloading method based on edge computing in the internet of vehicles (IoV) is proposed to address the dual challenges of low-latency service demands and energy conservation in the IoV. In the context of multivehicle single-cell scenarios on public roads, this study explores the task-offloading requirements of vehicles in motion and systematically investigates the allocation of computational resources. To fully utilize computing resources, this study not only considers the computing power of vehicles but also introduces a new approach for offloading tasks to vehicle servers traveling in the same direction or parked along the roadside as well as to edge servers in roadside units. This enables the effective integration and efficient sharing of computing resources, thereby remarkably enhancing the processing capabilities of the IoV. Furthermore, this study employs an improved particle swarm optimization algorithm to optimize offloading power and task allocation ratios. Extensive simulation tests revealed that the proposed method significantly reduced the energy consumption of vehicle tasks and improved the service quality and energy efficiency of the IoV.It helps to promote green transportation and sustainable development, and lays a solid foundation for energy optimization and efficiency improvement of future intelligent transportation systems.

Key words: internet of vehicles, edge computing, enhanced particle swarm algorithm, low energy