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Fog computing is one of the promising technology that could reduce the execution cost and energy consumption of smart industrial Internet of Things (IIoT) devices via a strategy called offloading. However, designing an intelligent offloading strategy for large-scale industrial applications becomes challenging. To address this issue, in this article, we design a novel fog federation, a computation offloading framework for industrial networks called cost-efficient computation offloading ( CeCO ), where a master fog controller regulates the network and distributes the IIoT data among the fog devices. In particular, we design our cost optimization function as the sum of weighted energy-delay cost of IIoT devices while reaching several constraints. To determine this optimization problem, we first design a frequency control mechanism for the IIoT devices. Then, we introduce a controller-based device adaptation strategy and a policy-based reinforcement learning technique for efficiently controlling emergency-based service demands and accordingly route them toward the fog devices following the shortest path. Experimental results demonstrate the effectiveness of the CeCO strategy then the baseline algorithms while maintaining the same and even better cost utilization and performance maximization upto 13%–18% for industrial applications.
Hazra et al. (Wed,) studied this question.