A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading

Zhao, Liang; Yang, Kaiqi; Tan, Zhiyuan; Li, Xianwei; Sharma, Suraj; Liu, Zhi

Abstract

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular Computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

Citation

Zhao, L., Yang, K., Tan, Z., Li, X., Sharma, S., & Liu, Z. (2021). A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3664-3674. https://doi.org/10.1109/TITS.2020.3024186

• Journal Article Type: Article
• Acceptance Date: Sep 8, 2020
• Online Publication Date: Sep 24, 2020
• Publication Date: 2021-06
• Deposit Date: Sep 8, 2020
• Publicly Available Date: Sep 24, 2020
• Journal: IEEE Transactions on Intelligent Transportation Systems
• Print ISSN: 1524-9050
• Electronic ISSN: 1558-0016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Issue: 6
• Pages: 3664-3674
• DOI: https://doi.org/10.1109/TITS.2020.3024186
• Keywords: Mobile edge computing; computation offloading; vehicular networks; unmanned aerial vehicles; game theory
• Public URL: http://researchrepository.napier.ac.uk/Output/2685307