D3QN-Enabled Diversified-Task Co-Offloading for Synthetic-Expense Minimization in Industrial Internet of Things (IIoT)

In recent years, during the neo-industrial revolution, mobile edge computing (MEC) offloading and peer offloading have received adequate attention in the Industrial Internet of Things (IIoT). In this article, we delve into task co-offloading on a single production line, which involves device-to-device (D2D) enabled peer offloading and 5G-enabled MEC offloading. The line conforms to industrial reality, with the focus on correlation, discrepancy of tasks, and industrial channel characteristics. However, the rapidly increasing transmission expense poses an obstacle to wireless offloading in IIoT. Confronting this thorny problem, we first construct a novel synthetic-expense (SE) function and accordingly deliberate the optimization problem as minimizing the sum of SE of diversified tasks. The SE minimization diversified-task co-offloading (SEIDEL) scheme is thoroughly designed afterward. Then, we conduct mathematical analyses of the corresponding offloading strategy, targeting different task categories, and introduce dueling double deep Q network (DQN) enabled SEIDEL (D3QN-SEIDEL) algorithm as the solution for the original nondeterministic polynomial hard problem. Eventually, the experiments based on a self-set production line demonstrate the excellent convergence of D3QN-SEIDEL and the superior performance of SEIDEL in handling D2D network fluctuations. Furthermore, the experiments reveal that SEIDEL offers a wide adjustable range for expense and latency, indicating significant potential for personalized applications. The experiments based on a practical line prove that SEIDEL exhibits higher potential ability in minimizing expense and delay in genuine IIoT scenarios.