Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 10; p. 3678
• Main Authors: Hossain, Md Delowar, Sultana, Tangina, Hossain, Md Alamgir, Layek, Md Abu, Hossain, Md Imtiaz, Sone, Phoo Pyae, Lee, Ga-Won, Huh, Eui-Nam
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.05.2022; MDPI
• Subjects: Algorithms; Collaboration; Communication; Computation offloading; Cooperation; Decision making; Failure rates; Game theory; multi-access edge computing; Response time; Servers; Strategy; task offloading; Traffic congestion; Vehicles; vehicular edge computing; multi-access edge computing; task offloading; vehicular edge computing; game theory
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22103678

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme.