Sub-Sampling Framework Comparison for Low-Power Data Gathering: A Comparative Analysis

Sensors

Bojan Milosevic¹  Carlo Caione¹  Elisabetta Farella¹  Davide Brunelli²  Luca Benini¹  Lavagno Luciano³ 

[1] DEI, University of Bologna, 40123 Bologna, Italy; E-Mails:;DII, University of Trento, I-38123 Trento, Italy; E-Mail:DEI, University of Bologna, 40123 Bologna, Italy;

关键词: wireless sensor networks;    low-power design;    data reconstruction;    compressive sensing;    latent variables;   

DOI  :  10.3390/s150305058

来源: mdpi

【 摘 要 】

A key design challenge for successful wireless sensor network (WSN) deployment is a good balance between the collected data resolution and the overall energy consumption. In this paper, we present a WSN solution developed to efficiently satisfy the requirements for long-term monitoring of a historical building. The hardware of the sensor nodes and the network deployment are described and used to collect the data. To improve the network's energy efficiency, we developed and compared two approaches, sharing similar sub-sampling strategies and data reconstruction assumptions: one is based on compressive sensing (CS) and the second is a custom data-driven latent variable-based statistical model (LV). Both approaches take advantage of the multivariate nature of the data collected by a heterogeneous sensor network and reduce the sampling frequency at sub-Nyquist levels. Our comparative analysis highlights the advantages and limitations: signal reconstruction performance is assessed jointly with network-level energy reduction. The performed experiments include detailed performance and energy measurements on the deployed network and explore how the different parameters can affect the overall data accuracy and the energy consumption. The results show how the CS approach achieves better reconstruction accuracy and overall efficiency, with the exception of cases with really aggressive sub-sampling policies.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.