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EnglishA key factor in a successful sensor network deployment is finding a good balance between maximizing the number of measurements taken (to maintain a good sampling rate) and minimizing the overall energy consumption (to extend the network lifetime). In this work, we present a data-driven statistical model to optimize this tradeoff. Our approach takes advantage of the multivariate nature of the data collected by a heterogeneous sensor network to learn spatio-temporal patterns. These patterns enable us to employ an aggressive duty cycling policy on the individual sensor nodes, thereby reducing the overall energy consumption. Our experiments with the OMNeT++ network simulator using realistic wireless channel conditions, on data collected from two real-world sensor networks, show that we can sample just 20% of the data and can reconstruct the remaining 80% of the data with less than 9% mean error, outperforming similar techniques such is distributed compressive sampling. In addition, energy savings ranging up to 76%, depending on the sampling rate and the hardware configuration of the node.
| Titolo: | Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization |
| Autore/i: | MILOSEVIC, BOJAN; Jinseok Yang; Nakul Verma; Sameer S. Tilak; Piero Zappi; FARELLA, ELISABETTA; BENINI, LUCA; Tajana Simunic Rosing |
| Autore/i Unibo: | |
| Anno: | 2013 |
| Titolo del libro: | Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems - MSWiM '13 |
| Pagina iniziale: | 247 |
| Pagina finale: | 254 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1145/2507924.2507953 |
| Abstract: | A key factor in a successful sensor network deployment is finding a good balance between maximizing the number of measurements taken (to maintain a good sampling rate) and minimizing the overall energy consumption (to extend the network lifetime). In this work, we present a data-driven statistical model to optimize this tradeoff. Our approach takes advantage of the multivariate nature of the data collected by a heterogeneous sensor network to learn spatio-temporal patterns. These patterns enable us to employ an aggressive duty cycling policy on the individual sensor nodes, thereby reducing the overall energy consumption. Our experiments with the OMNeT++ network simulator using realistic wireless channel conditions, on data collected from two real-world sensor networks, show that we can sample just 20% of the data and can reconstruct the remaining 80% of the data with less than 9% mean error, outperforming similar techniques such is distributed compressive sampling. In addition, energy savings ranging up to 76%, depending on the sampling rate and the hardware configuration of the node. |
| Data prodotto definitivo in UGOV: | 2-lug-2014 |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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