Agregação e Predição de Dados Em Rede com Precisão Ajustável no Processamento de Consultas em Redes de Sensores Sem Fio

Tales Benigno MatosAngelo BraynerJ. E. Bessa Maia

Over the past few years, many research works in Wireless Sensor Networks (WSN) have been focusing on node power saving. In order to achieve this goal, the amount of data sent over the node network is usually reduced. In this work, we propose an efficient strategy that aggregates and predicts data in WSN, aiming to reduce the data volume sent over the network and thus maximizing the network lifetime. Besides the widely used in-network aggregation strategy, this work presents the use of in-network prediction, based on query processing on the network data. Our prediction strategy works with a linear regression model, using data acquired from one or several sensor nodes. It is implemented in various sensor nodes distributed in a WSN. Experimental results show that our strategy is able to significantly reduce power consumption in WSN.

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