Fog Computing for Energy-Efficient Data Offloading of IoT Applications in Industrial Sensor Networks

Abstract

Fog computing has recently emerged to <i>in-situ</i> processing and energy-aware data offloading of Internet of Things (IoT) applications in the industrial sensor networks. Besides that, increasing the performance of large-scale IoT applications by improving the emergency response time has become a critical issue in sensor networks. To address the above-mentioned challenges, in this paper, we design a novel Energy-aware Data Offloading (<monospace>EaDO</monospace>) technique to minimize the energy consumption and latency in the industrial environment. The proposed <monospace>EaDO</monospace> strategy first outlines the emergency information of the incoming tasks with the attribute values. Next, the <monospace>EaDO</monospace> strategy schedules the emergency tasks using a multilevel feedback queuing policy to improve the schedulability. Moreover, a graph-theoretic approach, called as <i>Hall&#x2019;s</i> theorem is also adopted for finding maximum matching between scheduled tasks and active computing devices, including distributed fog devices and centralized cloud servers. Extensive simulation results exhibit that the <monospace>EaDO</monospace> strategy significantly improves the energy consumption rate of the industry generated tasks up to 23&#x0025;-30&#x0025; over the existing algorithms.