A Task Offloading Scheme for WAVE Vehicular Clouds and 5G Mobile Edge Computing

Vehicular applications are becoming increasingly popular. Some of them are known to be compute-intensive as to require real-time processing. One technique used to improve the performance of these applications is task offloading, which allows computational tasks to be processed on remote servers. In vehicular environments, these servers can be the vehicles themselves or edge servers coupled to base stations. But it is challenging to apply this technique in these environments, where frequent changes in network topology occur, and there is no central coordination point. Therefore, we propose a scheme to improve the performance of computationally intensive applications while dealing with the mobility challenges of vehicular environments. The proposed scheme runs on multi-interface networks WAVE and 5G and searches and prioritizes, through a lightweight greedy algorithm, for servers with high processing capabilities available and shorter distances. The results show that the proposed scheme reduces up to 54.1 % the total offloading time and increases up to 71.8 % the offloading success rate compared to other schemes. In addition, this is one of the first works to compare different task offloading schemes for vehicles using WAVE and 5G multi-interface networks.