Parking Cooperation-Based Mobile Edge Computing Using Task Offloading Strategy

Abstract

The surge in computing demands of onboard devices in vehicles has necessitated the adoption of mobile edge computing (MEC) to cater to their computational and storage needs. This paper presents a task offloading strategy for mobile edge computing based on collaborative roadside parking cooperation, leveraging idle computing resources in roadside vehicles. The proposed method establishes resource sharing and mutual utilization among roadside vehicles, roadside units (RSUs), and cloud servers, transforming the computing task offloading problem into a constrained optimization challenge. To address the complexity of this optimization problem, a novel Hybrid Algorithm based on the Hill-Climbing and Genetic Algorithm (HHGA) is proposed, combined with the powerful Simulated Annealing (SA) algorithm. The HHGA-SA Algorithm integrates the advantages of both HHGA and SA to efficiently explore the solution space and optimize task execution with reduced delay and energy consumption. The HHGA component of the algorithm utilizes the strengths of Genetic Algorithm and Hill-Climbing. The Genetic Algorithm enables global exploration, identifying potential optimal solutions, while Hill-Climbing refines the solutions locally to improve their quality. By harnessing the synergies between these techniques, the HHGA-SA Algorithm navigates the multi-constraint landscape effectively, producing robust and high-quality solutions for task offloading. To evaluate the efficacy of the proposed approach, extensive simulations are conducted in a realistic roadside parking cooperation-based Mobile Edge Computing scenario. Comparative analyses with standard Genetic Algorithms and Hill-Climbing demonstrate the superiority of the HHGA-SA Algorithm, showcasing substantial enhancements in task execution efficiency and energy utilization. The study highlights the potential of leveraging idle computing resources in roadside parking vehicles to enhance Mobile Edge Computing capabilities. The collaborative approach facilitated by the HHGA-SA Algorithm fosters efficient task offloading among roadside vehicles, RSUs, and cloud servers, elevating overall system performance.