Meta-heuristic Approach to Optimize Vehicular Task Offloading in 5G VEC Systems

Abstract

The advancement of vehicle technology enables it to handle complex tasks such as augmented reality, automatic parking, and driving, which demand significant computational power and are sensitive to delays. However, the computational capabilities of these vehicles are limited. As a result, Vehicle Edge Computing (VEC) has been introduced to offload some computational burdens to neighbouring vehicles and edge servers. The main challenge of this system lies in managing offloading in scenarios with frequent disconnections and high-speed driving. To address this challenge, we have formulated the task offloading problem with the goal of minimizing overall time, considering its NP-hard nature and multiple constraints. Our proposed solution is a meta-heuristic approach that integrates the honey bee Food Foraging process with Genetics Algorithm (FFGA) for vehicular task offloading. In the food foraging process, scouts are sent to locate nearby area, which in our case are edge servers and gather contextual details, and upon their return, they perform a dance resembling the infinity symbol, where the central angle indicates the direction of the food field and the speed of the dance signifies the quality of the found food source. The vehicle client uses this information to select one edge server to be in charge of the offloading process. Subsequently, we apply the genetic algorithm to generate an optimized allocation scheme of tasks by considering contextual details of nearby nodes. These details include computational capacity, availability, location, and driving speed. The optimization operators of the genetic algorithm are used to determine the assignment of tasks to nodes. Extensive simulations have demonstrated that the proposed FFGA system outperforms other existing schemes, including the hybrid vehicular edge cloud (HVC), particle swarm optimization (PSO), and the multi-decision based offloading (MDO).