Efficient Data Gathering in Wireless Sensor Networks Based on Matrix Completion and Compressive Sensing

TL;DR

This paper presents MCCS, a novel method combining matrix completion and compressive sensing to efficiently gather data in wireless sensor networks, reducing energy consumption and extending network lifetime.

Contribution

It introduces a new approach that leverages intra-temporal and inter-spatial correlations for energy-efficient data collection in sensor networks.

Findings

Significantly reduces data transmission in sensor networks.

Prolongs network lifetime through energy-efficient data gathering.

Validated with real datasets demonstrating effectiveness.

Abstract

Gathering data in an energy efficient manner in wireless sensor networks is an important design challenge. In wireless sensor networks, the readings of sensors always exhibit intra-temporal and inter-spatial correlations. Therefore, in this letter, we use low rank matrix completion theory to explore the inter-spatial correlation and use compressive sensing theory to take advantage of intra-temporal correlation. Our method, dubbed MCCS, can significantly reduce the amount of data that each sensor must send through network and to the sink, thus prolong the lifetime of the whole networks. Experiments using real datasets demonstrate the feasibility and efficacy of our MCCS method.