@Article{cmc.2024.050975,
AUTHOR = {Yu Zhou, Yun Zhang, Guowei Li, Hang Yang, Wei Zhang, Ting Lyu, Yueqiang Xu},
TITLE = {Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {80},
YEAR = {2024},
NUMBER = {2},
PAGES = {1809--1829},
URL = {http://www.techscience.com/cmc/v80n2/57592},
ISSN = {1546-2226},
ABSTRACT = {In current research on task offloading and resource scheduling in vehicular networks, vehicles are commonly assumed to maintain constant speed or relatively stationary states, and the impact of speed variations on task offloading is often overlooked. It is frequently assumed that vehicles can be accurately modeled during actual motion processes. However, in vehicular dynamic environments, both the tasks generated by the vehicles and the vehicles’ surroundings are constantly changing, making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios. Taking into account the actual dynamic vehicular scenarios, this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios, attempting to solve the dynamic decision-making problem in task offloading process. The optimization objective is to minimize the average task completion time, which is formulated as a multi-constrained non-linear programming problem. Due to the mobility of vehicles, a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission. Finally, the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient (MADDPG) is applied to solve the optimal solution of the optimization problem. Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading. Meanwhile, the average task completion time of the proposed algorithm in this paper can be improved by 7.6% compared to the performance of the MADDPG scheme and 51.1% compared to the performance of deep deterministic policy gradient (DDPG).},
DOI = {10.32604/cmc.2024.050975}
}