Query Age of Information: Freshness in Pull-Based Communication

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.

On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors

“…AoI-optimal scheduling has attracted a significant amount of interest from the research community over the last few years [4]- [27]. Particularly, a popular approach is to model the problem as an MDP and find an optimal policy by using model-based reinforcement learning (RL) methods based on dynamic programming [4], [5], [9]- [12], [15], [16], [18]- [20], [26], [27], e.g., relative value iteration algorithm (RVIA), and/or model-free RL methods [4], [9], [10], [14], [21], [22], e.g., (deep) Q-learning.…”

“…However, in contrast to our paper, the works [24], [25] do not consider energy limitation at the source nodes. In [26], [27], the authors introduced the AoI at query (QAoI) and developed an MDP-based policy iteration method to find an optimal policy that minimizes the average QAoI considering an energy-constrained sensor that is queried to send updates to an edge node under limited transmission opportunities. The QAoI metric [26], [27] is equivalent to our on-demand AoI when particularized to the single-user single-sensor case.…”