Closed-Loop Control of Edge-Cloud Collaboration Enabled IIoT: An Online Optimization Approach
You Shi, Changyan Yi, Bing Chen, Chenze Yang, Xiangping Zhai, Jun Cai · 2022
In this paper, an energy-efficient resource management framework for industrial Internet of Things (IIoT) with closed-loop control on end devices, edge servers (ESs) and cloud center (CC) is studied. In the considered model, each ES aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of i) sensors’ sampling rate adaption, ii) ESs’ preprocessing mode selection and iii) edge-cloud communication and computing resource allocation, is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Performance analyses and simulation results show that the proposed algorithm is superior compared to counterparts in terms of energy efficiency and delay performance under service satisfaction constraints.