Research Article
NOMA-Based Task Offloading and Allocation in Vehicular Edge Computing Networks
@INPROCEEDINGS{10.1007/978-3-031-24383-7_19, author={Shuangliang Zhao and Lei Shi and Yi Shi and Fei Zhao and Yuqi Fan}, title={NOMA-Based Task Offloading and Allocation in Vehicular Edge Computing Networks}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2023}, month={1}, keywords={VEC NOMA Task offloading Task allocation Collaborative processing}, doi={10.1007/978-3-031-24383-7_19} }- Shuangliang Zhao
Lei Shi
Yi Shi
Fei Zhao
Yuqi Fan
Year: 2023
NOMA-Based Task Offloading and Allocation in Vehicular Edge Computing Networks
COLLABORATECOM
Springer
DOI: 10.1007/978-3-031-24383-7_19
Abstract
Vehicular Edge Computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks. In the VEC system, vehicles can choose to upload tasks to nearby edge nodes for processing. This approach requires an efficient communication method, and Non-Orthogonal Multiple Access (NOMA) can improve channel spectrum efficiency and capacity. However, in the VEC system, the channel condition is complex due to the fast mobility of vehicles, and the arrival time of each task is stochastic. These characteristics greatly affect the latency of tasks. In this paper, we adopt a NOMA-based task offloading and allocation scheme to improve the VEC system. To cope with complex channel conditions, we use NOMA to upload tasks in batches. We first establish the mathematical model, and divide the offloading and allocation of tasks into two processes: transmission and computation. Then we determine appropriate edge nodes for transmission and computation according to the position and speed of vehicles. We define the optimization objective as maximizing the number of tasks completed, and find that it is an integer nonlinear problem. Since there are more integer variables, this optimization problem is difficult to solve directly. Through further analysis, we design Asymptotic Inference Greedy Strategy (AIGS) algorithm based on heuristics. Simulation results demonstrate that our algorithm has great advantages.
