Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization

Authors:
Bojan Milosevic;Jinseok Yang;Nakul Verma;Sameer S. Tilak;Piero Zappi;Elisabetta Farella;Luca Benini;Tajana Simunic Rosing
Affiliations:
University of Bologna, Bologna, Italy;University of California at San Diego, La Jolla, CA, USA;University of California at San Diego, La Jolla, CA, USA;University of California at San Diego, La Jolla, CAUSA;Qualcomm Inc., San Diego, USA;University of Bologna, Bologna, Italy;University of Bologna, Bologna, Italy;University of California at San Diego, La Jolla, CAUSA
Venue:
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
Year:
2013

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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.