Age Optimal Sampling Under Unknown Delay Statistics

Abstract

In this work, we consider a status update system with a sensor and a receiver. The status update information is sampled by the sensor and then forwarded to the receiver through a channel with non-stationary delay distribution. The data freshness at the receiver is quantified by the Age-of-Information (AoI). The goal is to design an online sampling strategy that can minimize the average AoI when the non-stationary delay distribution is unknown. Assuming that channel delay distribution may change over time, to minimize the average AoI, we propose a joint stochastic approximation and non-parametric change point detection algorithm that can: (1) learn the optimum update threshold when the delay distribution remains static; (2) detect the change in transmission delay distribution quickly and then restart the learning process. Simulation results show that the proposed algorithm can quickly detect the delay changes, and the average AoI obtained by the proposed policy converges to the minimum AoI.

Status Update Scheduling in Remote Sensing Under Variable Activation Delay

“…This research can be extended in several interesting ways. We could explore the impact of different types of delay on AoI, both qualitatively and quantitatively, for example considering different statistics and/or multiple delay terms such as sensing, transmission, and queueing delay [24], [28]. All of this would lead to different evaluations of the graphs in Fig.…”

“…Such an approach stands out as a possible solution for the problems identified in the present paper. Finally, [28] considers the impact of AoI of a variable delay with very general statistics. Even though that paper is similar in motivation to the present one, the development is different as they consider a queueing system and the delay is again meant there to increase the propagation time, not the activation time as we do here.…”

Impact of Transmission Delays Over Age of Information Under Finite Horizon Scheduling

“…Finally, our approach can be related to the extensive analysis performed in the very recent reference [32], which is the most comprehensive treatment of AoI under variable delay. The work considers an online sampling policy, as opposed to the offline scheduling tackled in this paper.…”

“…The work considers an online sampling policy, as opposed to the offline scheduling tackled in this paper. In other words, [32] proposes a strategy to decide, based on the currently experienced delay, when to perform the next update. Conversely, we assign multiple transmission instants at once, based on the a priori statistics of delay, so as to minimize AoI.…”