Optimized Data Aggregation in WSNs using Adaptive ARMA

Jia-Liang Lu Fabrice Valois 1 Mischa Dohler 2
1 SWING - Smart Wireless Networking
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Wireless sensor networks (WSNs) are data centric networks to which data aggregation is a central mechanism. Nodes in such networks are known to be of low complexity and highly constrained in energy. This requires novel distributed algorithms to data aggregation, where accuracy, complexity and energy need to be optimized in the aggregation of the raw data as well as the communication process of the aggregated data. To this end, we propose in this work a distributed data aggregation scheme based on an adaptive Auto-Regression Moving Average (ARMA) model estimation using a moving window technique and running over suitable communications protocols. In our approach, we balance the complexity of the algorithm and the accuracy of the model so as to facilitate the implementation. Subsequent analysis shows that an aggregation efficiency up to 60% can be achieved with a very fine accuracy of 0.03 degree. And simulation results confirm that this distributed algorithm provides significant energy savings (over 80%) for mass data collection applications.
Type de document :
Communication dans un congrès
Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM), Jul 2010, Venice, Italy. 2010
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https://hal.inria.fr/inria-00540854
Contributeur : Fabrice Valois <>
Soumis le : lundi 29 novembre 2010 - 13:02:03
Dernière modification le : vendredi 22 décembre 2017 - 11:22:11

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  • HAL Id : inria-00540854, version 1

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Jia-Liang Lu, Fabrice Valois, Mischa Dohler. Optimized Data Aggregation in WSNs using Adaptive ARMA. Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM), Jul 2010, Venice, Italy. 2010. 〈inria-00540854〉

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