[en] Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this article, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the near-optimal solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Raza, Salman
Wang, Shangguang
Ahmed, Manzoor
Anwar, Muhammad Rizwan
Mirza, Muhammad Ayzed
Khan, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Task Offloading and Resource Allocation for IoV Using 5G NR-V2X Communication
Alternative titles :
[en] Task Offloading and Resource Allocation for IoV Using 5G NR-V2X Communication
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