Data-Driven Edge Offloading for Wireless Control Systems

Abstract

As industrial plants embrace modern technologies, such as edge computing and wireless networks, industrial control systems have evolved into multi-tier cyber-physical systems. While traditional local controllers enjoy reliable connectivity to sensors/actuators, they suffer from the limited computation capacity of embedded devices. In contrast, edge servers introduce more computation resources connected to sensors/actuators through wireless networks. Offloading control functions to edge servers presents new opportunities to enhance control performance but also poses critical challenges. As wireless networks have limited bandwidth and varying reliability, it is important to optimize control performance by dynamically offloading a subset of the control functions to edge servers under the bandwidth constraint. Furthermore, the selection of offloaded control functions depends on both the cyber (wireless) and physical states of the wireless control systems. In this paper, we tackle the problem of optimizing the control performance of multiple control loops through dynamic edge offloading. We establish a data-driven model to predict the control performance of each feedback control loop based on its cyber-physical states. We then develop a dynamic edge offloading approach to optimize the overall control performance of a system with multiple feedback control loops while guaranteeing their stability under fluctuating cyber-physical conditions. Finally, we demonstrate the efficacy of the data-driven model and offloading approach in case studies comprising simulations of up to twenty industrial robots.