Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks

TL;DR

This paper introduces a novel compressive sensing scheme for wireless sensor networks that efficiently acquires spatially sparse fields, reducing energy and bandwidth consumption while maintaining accurate signal reconstruction.

Contribution

It presents a structured CS matrix for bidimensional sparse signals and analytically evaluates its energy and bandwidth efficiency in WSN data acquisition.

Findings

Achieves significant energy savings compared to existing methods.

Reduces bandwidth usage while maintaining accurate signal reconstruction.

Demonstrates effectiveness through numerical simulations.

Abstract

Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically evaluate the energy and bandwidth consumption of our CS scheme when it is applied to data acquisition in a WSN. Numerical results demonstrate that our CS scheme achieves significant energy and bandwidth savings wrt state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN.