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 AutoregRessive Integrated Moving Average 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.
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Zhang, H., Zheng, J., Han, Q., Song, B., Wang, H. (2013). Prediction Based Quantile Filter for Top-k Query Processing in Wireless Sensor Networks. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_44
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DOI: https://doi.org/10.1007/978-3-642-39482-9_44
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