Sensor-field modeling based on in-network data prediction: an efficient strategy for answering complex queries in wireless sensor networks

Authors:
Jose Everardo Bessa Maia;Angelo Brayner
Affiliations:
State University of Ceara - UECE, Fortaleza - Ceara - Brazil;University of Fortaleza - UNIFOR, Fortaleza - Ceara - Brazil
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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Abstract

In this work, we present a mechanism, denoted ADAGA -- P, for managing sensor-field regression models. ADAGA -- P implements an in-network data prediction mechanism in order to only transmit data which are novelties for a regression model applied by ADAGA -- P. Experiments using real data have been executed to validate our approach. The results show that ADAGA -- P is quite efficient regarding communication cost and the number of executed float-point operations. In fact, the energy consumption rate to run ADAGA -- P is 15 times lower than the energy consumed by kernel distributed regression for an RMSE difference of 0.003.