Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization
| Autor: | Nakul Verma, Sameer Tilak, Bojan Milosevic, Luca Benini, Jinseok Yang, Tajana Rosing, Piero Zappi, Elisabetta Farella |
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| Přispěvatelé: | Bojan Milosevic, Jinseok Yang, Nakul Verma, Sameer S. Tilak, Piero Zappi, Elisabetta Farella, Luca Benini, Tajana Simunic Rosing |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
business.industry Energy management Node (networking) Real-time computing 020206 networking & telecommunications Statistical model 02 engineering and technology Energy consumption 020202 computer hardware & architecture Data recovery Network simulation 0202 electrical engineering electronic engineering information engineering tensor factorization ENERGY MANAGEMENT LATENT VARIABLES business Wireless sensor network Efficient energy use data recovery |
| Zdroj: | MSWiM |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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