Comparison of different data prediction methods for wireless sensor networks

Abstract

Different data reduction strategies have been developed in order to reduce the energy consumption in wireless sensor networks (WSN). Most of them reduce the amount of sent data by predicting the measured values both at the source and the sink, requiring transmission only if a certain reading differs by a given margin from the predicted values. The subject of this paper is comparison of a few different techniques for prediction of time series data in WSN. While these strategies often provide great reduction in power consumption, they don’t need a priori knowledge of the explored domain in order to correctly model the expected values.