Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China.
School of Mechanical and Electrical Engineering, Beijing University of Technology, Beijing 100081, China.
Sensors (Basel). 2022 Jun 30;22(13):4959. doi: 10.3390/s22134959.
Due to the limited computing capacity of onboard devices, they can no longer meet a large number of computing requirements. Therefore, mobile edge computing (MEC) provides more computing and storage capabilities for vehicles. Inspired by a large number of roadside parking vehicles, this paper takes the roadside parking vehicles with idle computing resources as the task offloading platform and proposes a mobile edge computing task offloading strategy based on roadside parking cooperation. The resource sharing and mutual utilization among roadside vehicles, roadside units (RSU), and cloud servers (cloud servers) were established, and the collaborative offloading problem of computing tasks was transformed into a constraint problem. The hybrid genetic algorithm (HHGA) with a mountain-climbing operator was used to solve the multi-constraint problem, to reduce the delay and energy consumption of computing tasks. The simulation results show that when the number of tasks is 25, the delay and energy consumption of the HHGA algorithm is improved by 24.1% and 11.9%, respectively, compared with Tradition. When the task size is 1.0 MB, the HHGA algorithm reduces the system overhead by 7.9% compared with Tradition. Therefore, the proposed scheme can effectively reduce the total system cost during task offloading.
由于车载设备的计算能力有限，它们已经无法满足大量的计算需求。因此，移动边缘计算（MEC）为车辆提供了更多的计算和存储能力。受大量路边停车车辆的启发，本文以具有空闲计算资源的路边停车车辆作为任务卸载平台，并提出了一种基于路边停车协作的移动边缘计算任务卸载策略。建立了路边车辆、路边单元（RSU）和云服务器（云服务器）之间的资源共享和相互利用，将计算任务的协作卸载问题转化为约束问题。使用带有爬山算子的混合遗传算法（HHGA）来解决多约束问题，以降低计算任务的延迟和能耗。仿真结果表明，当任务数量为 25 时，与传统算法相比，HHGA 算法的延迟和能耗分别提高了 24.1%和 11.9%。当任务大小为 1.0MB 时，HHGA 算法与传统算法相比，系统开销降低了 7.9%。因此，所提出的方案可以有效地降低任务卸载过程中的总系统成本。
