Tasks Offloading in Vehicular Edge Computing Network using Meta-Heuristic Algorithms - A Study of Selected Algorithms

By utilizing the computational resources at the network edge, Vehicular Edge Computing (VEC) has become a viable paradigm to handle the computational demands of developing vehicular applications. A key component of VEC, task offloading seeks to improve vehicle services’ Quality of Service (QoS) and maximize resource efficiency. This paper provides a thorough analysis of recent developments in VEC framework task offloading strategies, with an emphasis on the incorporation of meta heuristic algorithmic techniques. The review explains the opportunities and challenges in this field while delving into the basic ideas of task offloading and VEC. It looks at different meta heuristic algorithms such as Simulated Annealing (SA), Ant Colony optimization (ACO), Particle Swarm optimization (PSO), and Genetic Algorithms (GA) and how to use them to optimize task offloading choices in VEC settings. This review also identifies emerging trends and challenges in the integration of meta heuristic algorithms for task offloading in VEC, as well as highlighting current research efforts in this area. It talks about important performance metrics like latency, energy efficiency, and reliability and assesses how well meta heuristic-based methods work to improve these metrics. In addition, it addresses open research questions and possible future paths towards the advancement of task offloading methods in VEC. Overall, this review offers practitioners and researchers useful insights into the role of meta heuristic algorithms in task offloading processes optimization in vehicular edge computing environments, guiding the development of dependable and effective vehicular edge computing systems.