# Insense: Incoherent Sensor Selection for Sparse Signals

## TL;DR

Insense is a novel sensor selection algorithm designed for sparse signals, optimizing sensor subsets to enable accurate recovery from fewer measurements than the signal dimension, outperforming existing methods.

## Contribution

The paper introduces Insense, a new sensor selection algorithm based on coherence minimization, effective for sparse signals and applicable where traditional methods fail.

## Key findings

- Insense outperforms existing algorithms on six datasets.
- Effective in reducing sensors needed for sparse signal recovery.
- Validated on real-world applications like microbial diagnostics.

## Abstract

Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor Selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using six datasets, including two real-world datasets on microbial diagnostics and structural health monitoring, we demonstrate the superior performance of Insense for sparse-signal sensor selection.