AoI-Aware Sampling, Transmission, and Computation for Edge-Enabled Control System

In this paper, an age of information (AoI)-aware joint design framework of sampling, transmission, computation, and control is considered for industrial cyber-physical systems. To enhance the control performance, we investigate an edge-enabled control scheme, which allows a physical entity to select its sampling adaptively and processing strategies based on de-mand. By analyzing the impact of sampling and short-packet decoding errors, and the coupling relationship between control accuracy and AoI, the AoI -aware control metric is established. Subsequently, we formulate a joint sampling time, computation offloading, and bandwidth allocation optimization problem to minimize the system's control and energy costs. To tackle the formulated NP-hard problem, we develop a BCD-based algorithm leveraging convex and game theories to obtain a joint optimization strategy in an iterative manner. Finally, the performance of the proposed edge-enabled control scheme is verified in the simulation results.