Adaptive Filter Updating for Energy-Efficient Top-k Queries in Wireless Sensor Networks Using Gaussian Process Regression

Adopting filtering mechanism of dynamic filtering windows installed on sensor nodes to process top-k queries is an important research direction in wireless sensor networks. The mechanism can reduce transmissions of redundant data by utilizing filters. However, existing algorithms based on filters consume a vast amount of energy due to filter updating. In this paper, an energy-efficient top-k query technique based on adaptive filters is proposed. Due to updating filters consuming a large amount of energy, an algorithm named FUGPR based on Gaussian process regression to process top-k queries is provided for saving energy. When the filters change, the sensor readings are predicted to calculate the updating costs of filters; then FUGPR decides whether the filters need to be updated or not. Thus, the energy consumption for updating filters is decreased. Experimental results show that our approach can reduce energy consumption efficiently for updating filters on two distinct real datasets.