Cooperative Transmission Scheduling and Computation Offloading With Collaboration of Fog and Cloud for Industrial IoT Applications

Abstract

Energy consumption for large amounts of delay-sensitive applications brings serious challenges with the continuous development and diversity of Industrial Internet of Things (IIoT) applications in fog networks. In addition, conventional cloud technology cannot adhere to the delay requirement of sensitive IIoT applications due to long-distance data travel. To address this bottleneck, we design a novel energy-delay optimization framework called transmission scheduling and computation offloading (TSCO), while maintaining energy and delay constraints in the fog environment. To achieve this objective, we first present a heuristic-based transmission scheduling strategy to transfer IIoT-generated tasks based on their importance. Moreover, we also introduce a graph-based task-offloading strategy using constrained-restricted mixed linear programming to handle high traffic in rush-hour scenarios. Extensive simulation results illustrate that the proposed TSCO approach significantly optimizes energy consumption and delay up to 12%-17% during computation and communication over the traditional baseline algorithms.