MOVE: Matching Game for Partial Offloading in Vehicular Edge Computing
Published:
Authors
Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, and Sajal Das
Conference
2024 IEEE International Conference on Communications (ICC), Denver, CO, USA (Accepted For Publication)
Abstract
Autonomous Vehicles (AVs) require substantial computational resources to perform operations that safely navigate vehicles in urban road networks. Resource-intensive operations are offloaded to roadside units (RSUs), acting as edge servers, to improve the responsiveness and reduce the energy consumed in execution. In this context, a cooperative execution involving the vehicular on-board units (OBUs) and the RSUs can act as a game changer. However, partial offloading is non-trivial and demands addressing the following research challenges. Firstly, the RSU’s resources are limited, necessitating regulated resource assignments. Secondly, capturing distinctive vehicle parameters using a unified ranking scheme is imperative. Thirdly, an efficient partition strategy must consider the energy expended and adhere to the real-time operations’ deadline needs. This paper proposes a partial offloading scheme, MOVE, catering to the abovementioned challenges. A deferred acceptance algorithm (DAA) with preferences is proposed to address the first two challenges, whereas a novel energy-aware partitioning strategy resolves the final challenge. The performance of the proposed scheme is evaluated against baseline algorithms, and we observed a 54.04% and 52.17% reduction in offloading latency and energy.
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