A DRL-based multi-priority task division scheduling strategy in IIoT

The industrial internet of things (IIoT) system based on the multi-access edge computing (MEC) network architecture can significantly enhance industrial production efficiency and drive the advancement of smart manufacturing. However, such a system faces uncertain environmental factors, including dynamic changes in channel conditions and the random generation of tasks. Motivated by these challenges, this paper investigates the problem of task division and offloading decisions for delay-sensitive tasks with prioritization attributes from the perspectives of low latency and high value, and proposes a twin delayed deep deterministic based multi-prioritized task division scheduling (TD3-MPTDS) strategy in the device to device (D2D)-assisted MEC network. This strategy not only divides tasks into smaller chunks, enabling finer-grained scheduling of the entire system, but also intelligently offloads tasks to either the D2D network or the MEC server while considering the overall device load. In addition, the proposed strategy is tailored to optimize the task queue of the MEC server. By considering factors such as priority, waiting time, and expected time of completion, queue adjustments are dynamically made at each time slot. Simulation experiments validate that our proposed strategy quickly converges and outperforms the benchmark strategies in terms of task completion delay and completion value.