On the Uncertainty of a Simple Estimator for Remote Source Monitoring over ALOHA Channels

TL;DR

This paper analyzes the uncertainty in remote source monitoring over ALOHA channels, revealing that throughput maximization does not always minimize uncertainty and that mixed strategies can be beneficial for asymmetric sources.

Contribution

It provides an analytical characterization of uncertainty in remote monitoring over slotted ALOHA channels and evaluates different access strategies for minimizing this uncertainty.

Findings

Throughput maximization does not always reduce uncertainty.

Reactive policies are optimal for symmetric sources.

Asymmetric sources benefit from mixed transmission strategies.

Abstract

Efficient remote monitoring of distributed sources is essential for many Internet of Things (IoT) applications. This work studies the uncertainty at the receiver when tracking two-state Markov sources over a slotted random access channel without feedback, using the conditional entropy as a performance indicator, and considering the last received value as current state estimate. We provide an analytical characterization of the metric, and evaluate three access strategies: (i) maximizing throughput, (ii) transmitting only on state changes, and (iii) minimizing uncertainty through optimized access probabilities. Our results reveal that throughput optimization does not always reduce uncertainty. Moreover, while reactive policies are optimal for symmetric sources, asymmetric processes benefit from mixed strategies allowing transmissions during state persistence.