Prediction based quantile filter for top-k query processing in wireless sensor networks

Authors:
Hui Zhang;Jiping Zheng;Qiuting Han;Baoli Song;Haixiang Wang
Affiliations:
Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, P.R. China;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, P.R. China,State Key Laboratory for Novel Software Technology, Nanjing University, P.R.China;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, P.R. China;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, P.R. China;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, P.R. China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

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Abstract

Processing top-k queries in energy-efficient manner is an important topic in wireless sensor networks. It can keep sensor nodes from transmitting redundant data to base station by filtering methods utilizing thresholds on sensor nodes, which decreases the communication cost between the base station and sensor nodes. Quantiles installed on sensor nodes as thresholds can filter many unlikely top-k results from transmission for saving energy. However, existing quantile filter methods consume much energy when getting the thresholds. In this paper, we develop a new top-k query algorithm named QFBP which is to get thresholds by prediction. That is, QFBP algorithm predicts the next threshold on a sensor node based on historical information by A utoregR essive I ntegrated M oving A verage models. By predicting using ARIMA time series models, QFBF can decrease the communication cost of maintaining thresholds. Experimental results show that our QFBP algorithm is more energy-efficient than existing quantile filter algorithms.