Query Age of Information: Freshness in Pull-Based Communication

TL;DR

This paper introduces Query Age of Information (QAoI), a new metric for measuring information freshness in pull-based communication systems, optimizing query timing to improve data relevance.

Contribution

It proposes a novel QAoI metric tailored for pull-based systems and demonstrates its advantages over traditional AoI in optimizing information freshness.

Findings

QAoI-aware optimization reduces average age significantly.

QAoI improves worst-case age in query-based systems.

Optimization differs notably from push-based AoI strategies.

Abstract

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case: the instants at which information is collected and used are dependent on a certain query process. We propose a model that accounts for the discrete time nature of many monitoring processes, considering a pull-based communication model in which the freshness of information is only important when the receiver generates a query: if the monitoring process is not using the value, the age of the last update is irrelevant. We then define the Age of Information at Query (QAoI), a more general metric that fits the pull-based scenario, and show how its optimization can lead to very different choices from traditional push-based AoI optimization when using a Packet Erasure Channel (PEC) and with limited link availability. Our results show that QAoI-aware optimization can significantly reduce the average and worst-case perceived age for both periodic and stochastic queries.