Abstract
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.
Highlights
The recent technological evolution of sensing devices for wireless sensor networks (WSNs) has triggered research activities in the field of data gathering and compression
We describe a real deployment of a WSN for the environmental monitoring of a heritage building, which is used as a common platform and testbed for the evaluation
Another data acquisition protocol for WSNs is presented in [19], where the results from studies in vehicle routing are applied to data collection in WSN and integrated with compressive sensing (CS). This approach is NP-hard, and the authors propose both a centralized and a distributed heuristic to reduce computational times and to improve the scalability of the approach. These works analyze the routing and aggregation problem in WSNs, while in our work, we focus on the spatio-temporal correlation in the observed phenomena, which can be used to improve the data reconstruction accuracy
Summary
The recent technological evolution of sensing devices for wireless sensor networks (WSNs) has triggered research activities in the field of data gathering and compression. To compare the performance of the two techniques, evaluating the best trade-off between signal recovery and energy savings, we explore the impact of features, such as the use of network-wide correlations to improve the reconstruction accuracy or the length of the data block to be reconstructed. For both the hardware characteristics and the gathered data, we use a real deployed sensor network.
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