A Large Deviations Approach to Sensor Scheduling for Detection of Correlated Random Fields

TL;DR

This paper develops a large deviations framework to optimize sensor scheduling for detecting correlated random fields, revealing how sensor spacing impacts detection performance at different SNR levels.

Contribution

It provides a closed-form expression for the error exponent of miss probability, linking sensor spacing and SNR, and characterizes optimal spacing strategies.

Findings

Error exponent increases with sensor spacing at high SNR.

At low SNR, there exists an optimal sensor spacing.

Closed-form expression for error exponent derived.

Abstract

The problem of scheduling sensor transmissions for the detection of correlated random fields using spatially deployed sensors is considered. Using the large deviations principle, a closed-form expression for the error exponent of the miss probability is given as a function of the sensor spacing and signal-to-noise ratio (SNR). It is shown that the error exponent has a distinct characteristic: at high SNR, the error exponent is monotonically increasing with respect to sensor spacing, while at low SNR there is an optimal spacing for scheduled sensors.