Research Area:  Fog Computing
Fog computing emerges as a promising mode to meet the stringent requirement of low latency in industrial Internet of Things (IIoT). By offloading partial computation-intensive tasks from fog node to cloud server, the computation experience of users can be further improved in fog computing system. In this paper, we develop an energy-efficient computation offloading scheme for IIoT in fog computing scenario. The purpose is to minimize energy consumption when computation tasks are accomplished within a desired energy overhead and delay. It has a comprehensive consideration on the components of energy consumption at fog node, which includes the energy consumption of local computing, transmitting and waiting states. To address this energy minimization problem, an accelerated gradient algorithm is proposed, it can find the optimal offloading ratio with a fast speed that improves the convergence speed of traditional method. Finally, the numerical results reveal that the proposed offloading scheme is superior to the local computing and full offloading schemes in terms of energy consumption and completion time, and further confirm the advantage of convergence rate.
Author(s) Name:  Siguang Chen; Yimin Zheng; Kun Wang; Weifeng Lu
Conferrence name:  IEEE International Conference on Communications (ICC)
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/document/8761199