MMTO: Multi-Vehicle Multi-Hop Task Offloading in MEC-Enabled Vehicular Networks

Abstract

Mobile Edge Computing (MEC)-enabled vehicular networks have emerged as a promising approach to enhancing the performance and efficiency of the Internet-of-Vehicles (IoV) applications. By leveraging some vehicles to act as transmission relays, multi-hop task offloading addresses the problem of intermittent connectivity between vehicles and edge servers to cope with the issues of network congestion or obstacles. However, two critical issues, i.e., uncooperative behaviors of selfish vehicles and network resource dynamics, resulting from multi-vehicle concurrent offloading are not fully considered in the existing work. To fill this gap, this paper proposes a novel and efficient task offloading scheme, namely MMTO, that exploits the multi-hop computational resources to maximize the system-wide profit, and supports incentive compatibility of vehicular users and concurrent offloading. Specifically, an iterative hierarchical estimation algorithm is designed to estimate the offloading delay and energy cost in order to iteratively optimize the offloading decisions. An energy-efficient routing approach is then proposed to schedule the transmission paths for the offloading vehicles. Furthermore, an effective reward-driven auction-based incentive mechanism is designed for incentivizing relayers and calculators to engage in collaboration. Both simulation and field experiments are conducted; extensive results demonstrate that MMTO outperforms the state-of-the-art approaches in terms of the system-wide profit improvement and overall task delay reduction.