Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading

TLDR

Cloud-based vehicular networks promise improved services by distributing computation between remote clouds and local vehicles. The study proposes a mobile‑edge computing off‑loading framework to further reduce latency and transmission costs in vehicular networks. The framework evaluates V2I and V2V transfer strategies and introduces a predictive combination‑mode relegation scheme that adaptively offloads tasks to MEC servers via direct upload or predictive relay transmissions. Illustrative results show the scheme substantially lowers computation cost and boosts task transmission efficiency.

Abstract

Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.