Abstract

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 ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CeCO</monospace> ), 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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CeCO</monospace> strategy then the baseline algorithms while maintaining the same and even better cost utilization and performance maximization upto 13%–18% for industrial applications.

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