工业物联网中基于任务紧急程度的资源分配算法

Task urgency-based resource allocation algorithm in industrial Internet of things

工业物联网中任务的生成通常具有连续性和周期性,并且任务对时延要求很高,这给系统成本带来了挑战。为应对这一挑战,提出了一种基于任务紧急程度的成本最小化资源分配算法。通过遗传算法优化任务的卸载策略和系统的资源分配策略,对于卸载的任务,根据任务的紧急程度进行调度,并在满足时延要求的前提下计算任务的最优发射功率。仿真结果表明,所提算法有效改善了系统总能耗成本。

In the industrial Internet of things,the generation of tasks is observed to exhibit both continuity and period‐icity,along with stringent latency requirements.These characteristics posed challenges to system’s cost-efficiency.To address these challenges,a cost minimization resource allocation algorithm based on the urgency of tasks was pro‐posed.By employing a genetic algorithm,the task offloading strategy and the system’s resource allocation strategy were optimized.For offloaded tasks,they were scheduled according to their level of urgency.Additionally,the opti‐mal transmission power for each task was calculated to meet latency constraints.Simulation results indicate that the proposed algorithm effectively reduces the overall energy cost of the system.