Extremum-Based Joint Compression and Detection for Distributed Sensing
Provides a simple, analyzable solution for distributed detection under communication constraints, relevant to IoT and sensor networks.
The paper proposes an extremum-based joint compression and detection strategy for distributed sensing with a k-bit one-way link, deriving exact nonasymptotic error probabilities and validating via simulations.
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.