Joint Communication, Computation, and Control for Computational Task Offloading in Vehicle-Assisted Multi-Access Edge Computing

• Published in: IEEE access Vol. 10; pp. 122513 - 122529
• Main Authors: Dang, Tri Nguyen, Manzoor, Aunas, Tun, Yan Kyaw, Kazmi, S. M. Ahsan, Haw, Rim, Hong, Sang Hoon, Han, Zhu, Hong, Choong Seon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; alternating direction method of multipliers (ADMM); Augmented reality; Cloud computing; collaborative V2Vs-assisted MEC system; Communication; Computation offloading; Computational modeling; Edge computing; Electric vehicles; Energy consumption; Interference; interference management; Mixed integer; Mobile computing; Multi-access edge computing (MEC); Power control; Relaxation method (mathematics); resource allocation; Resource management; Roads; Servers; tasks offloading; V2V communication; Vehicular ad hoc networks; Virtual reality
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3220251

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.