# Query Age of Information: Freshness in Pull-Based Communication

arXiv: https://arxiv.org/abs/2105.06845 · HTML: https://mlsanity.com/paper/2105.06845/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2021.

## Built on by (most cited first, 5 of 5)

- [Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches](https://mlsanity.com/paper/2310.09506/) (2023)
- [Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication](https://mlsanity.com/paper/2407.14092/) (2024)
- [Pull-Based Query Scheduling for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2503.06725/) (2025)
- [The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics](https://mlsanity.com/paper/2401.10265/) (2024)
- [Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI](https://mlsanity.com/paper/2505.06025/) (2025)

## Abstract (arXiv)

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.