Task urgency-based resource allocation algorithm in industrial Internet of things - ZOU Hong; ZHUO Sai; ZHANG Hong; ZHANG Mingxing; WU Dapeng (Dianxin kexue)
In the industrial Internet of things, the generation of tasks is observed to exhibit both continuity and periodicity, 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 proposed. 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 optimal 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.