Timely Group Updating

TL;DR

This paper introduces a group updating scheme for tracking anomalies or infections in large populations, optimizing group sizes to minimize information age, and demonstrating improvements over sequential methods when event probability is low.

Contribution

It proposes a novel group updating approach based on age of information, optimizing group sizes for minimal information delay, extending group testing concepts.

Findings

Optimal group size reduces age of information when event probability is small.

Group updating outperforms sequential updating in low-probability scenarios.

The method effectively tracks node statuses with improved timeliness.

Abstract

We consider two closely related problems: anomaly detection in sensor networks and testing for infections in human populations. In both problems, we have $n$ nodes (sensors, humans), and each node exhibits an event of interest (anomaly, infection) with probability $p$. We want to keep track of the anomaly/infection status of all nodes at a central location. We develop a $group$ $updating$ scheme, akin to group testing, which updates a central location about the status of each member of the population by appropriately grouping their individual status. Unlike group testing, which uses the expected number of tests as a metric, in group updating, we use the expected age of information at the central location as a metric. We determine the optimal group size to minimize the age of information. We show that, when $p$ is small, the proposed group updating policy yields smaller age compared to a sequential updating policy.