Joint communication, computation, and control for computational task offloading in vehicle-assisted multi-access edge computing

Dang, Tri Nguyen; Manzoor, Aunas; Tun, Yan Kyaw; Kazmi, S. M. Ahsan; Haw, Rim; Hong, Sang Hoon; Han, Zhu; Hong, Choong Seon

Abstract

Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of novel infotainment services such as Augmented Reality, Virtual Reality-based services will make the MEC and Fog resources insufficient. To cope with this issue, we propose a system model with onboard computation offloading, where an EV can utilize its neighboring EVs resources that are not resource-constrained to enhance its computing capacity. Then, we propose to solve the problem of computational task offloading by jointly considering the communication, computation, and control in a mobile vehicular network. We formulate a mixed-integer non-linear problem (MINLP) to minimize the trade-off between latency and energy consumption subject to the network resources and the mobility of EVs. The formulated problem is solved via the block coordination descent (BCD) method. In such a way, we decompose the original MINLP problem into three subproblems which are resource block allocation (RBA), power control and interference management (PCP), and offload decision problem (ODP). We then alternatively obtain solutions of RBA and PCP via the duality theory, and the third sub-problem is solvable via the relaxation method and alternating direction Lagrangian multiplier method (ADMM). Numerical results reveal that the proposed solution BCD-based algorithm performs a fast convergence rate.

Citation

Dang, T. N., Manzoor, A., Tun, Y. K., Kazmi, S. M. A., Haw, R., Hong, S. H., …Hong, C. S. (2022). Joint communication, computation, and control for computational task offloading in vehicle-assisted multi-access edge computing. IEEE Access, 10, 122513-122529. https://doi.org/10.1109/ACCESS.2022.3220251

• Journal Article Type: Article
• Acceptance Date: Oct 28, 2022
• Online Publication Date: Nov 7, 2022
• Publication Date: Nov 28, 2022
• Deposit Date: Jun 24, 2023
• Publicly Available Date: Jun 28, 2023
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Pages: 122513-122529
• DOI: https://doi.org/10.1109/ACCESS.2022.3220251
• Keywords: Multi-access edge computing (MEC), collaborative V2Vs-assisted MEC system, tasks offloading, resource allocation, alternating direction method of multipliers (ADMM), interference management, V2V communication
• Public URL: https://uwe-repository.worktribe.com/output/10582680
• Publisher URL: https://ieeexplore.ieee.org/document/9940624