Prediction-Based Filter Updating Policies for Top-k Monitoring Queries in Wireless Sensor Networks

Processing top- k query in an energy-efficient manner is an important topic in wireless sensor networks. Redundant data transmitting between base station and sink node is avoided by installing filters on sensor nodes; thus, communication overhead between base station and sensor nodes is decreased. However, existing algorithms such as FILA, and DAFM consume much energy when updating the filter window. In this paper, we propose a new top- k algorithm named PreFU which is based on prediction models to update window parameters of filters. PreFU can predict the next s step sensor values based on time series predicting models which can be built by historical data. By estimating the cost of updating window parameters based on predicted sensor values, updates of filter window parameters can be reduced. Thus, the cost of updating window parameters is decreased. Experimental results show that our PreFU algorithm is more energy-efficient than existing algorithms while guaranteeing the accuracy of top- k query results.