Optimizing Computational Efficiency in Autonomous Vehicles: Integrative Edge and Cloud Computing Strategies in Vehicular Networks
T.-C. Yu · 2024
The computational demands of autonomous electric vehicles within vehicular edge computing networks have surged in recent years. This research addresses this issue by proposing an integrated framework that combines mobile edge computing, cloud computing, and vehicular ad-hoc networks. This framework aims to distribute computational tasks efficiently across local, edge, and cloud servers, thus enhancing system performance and reducing latency. A selection of algorithms—rooted in non-cooperative game theory, the 0-1 knapsack problem, and resource scheduling—guides the offloading process to ensure optimal task allocation. The efficacy of the proposed solution is substantiated through comprehensive experimentation, demonstrating notable reductions in system overhead and improvements in service quality. The outcomes of this study make a significant contribution to vehicular edge computing, offering a robust solution to meet the sophisticated computational demands of modern autonomous vehicles.