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

Fog computing has recently emerged to in-situ 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 ( EaDO ) technique to minimize the energy consumption and latency in the industrial environment. The proposed EaDO strategy first outlines the emergency information of the incoming tasks with the attribute values. Next, the EaDO strategy schedules the emergency tasks using a multilevel feedback queuing policy to improve the schedulability. Moreover, a graph-theoretic approach, called as Hall’s 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 EaDO strategy significantly improves the energy consumption rate of the industry generated tasks up to 23%-30% over the existing algorithms.