Extremum-Based Joint Compression and Detection for Distributed Sensing

TL;DR

This paper introduces an extremum-based joint compression and detection method for distributed sensing systems, optimizing communication and decision accuracy in IoT localization scenarios.

Contribution

It proposes a simple extremum-based strategy with exact probability analysis, advancing efficient detection under low-rate communication constraints.

Findings

Exact false-alarm and misdetection probabilities derived.

Validation through simulations confirms theoretical analysis.

Abstract

We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a $k$-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a $k$-bit description to help the other decide whether their observations share a common underlying signal or are statistically independent. We propose a simple extremum-based strategy, in which the encoder sends the index of its largest sample and the decoder performs a scalar threshold test. We derive exact nonasymptotic false-alarm and misdetection probabilities and validate the analysis with representative simulations.