Adaptive Sampling for Age of Information in Non-Stationary Network Traffic

Abstract

Real-time status updates play an important role in low-latency cyber-physical systems, in which the real network traffic statistics (i.e., transmission delay and/or error rate) are often unknown and non-stationary. In such cases, short-time age-of-information (ST-AoI) is more crucial than long-term average AoI, because instantaneous high ST-AoI could lead to system failures even if the long-term average AoI is low. In this paper, we propose an adaptive sampling control (ASC) scheme to ensure a low ST-AoI outage probability, defined as the probability of the average AoI in each control cycle, i.e., over a limited number of packets, exceeding a given threshold. This ASC scheme does not rely on an explicit statistical model for the non-stationary traffic behaviors. It establishes a dynamic linearization data model with a pseudo-partial derivative (PPD) parameter to capture the unknown and non-stationary traffic statistics. By estimating the PPD parameter in each control cycle, ASC can determine the sampling rates to ensure an extremely low ST-AoI outage probability. Both numerical simulation and real-world experiment show that the proposed ASC scheme significantly outperforms existing methods, reducing the ST-AoI outage probability almost by half.