EFFECT: An energy efficient framework for data compression in tree-based wireless sensor networks

Periodic data collection is an important application in wireless sensor networks (WSNs). Since sensors are power constrained, building energy efficient data collection topology offers significant challenge. Compression of correlated data is one of the widely used techniques in WSNs where sensory data are compressed along their routes toward the sink. Consequently, a data compression tree is formed in which a sensor (say, child) selects its parent based on the degree of correlation among their sensed data, and the data of the child node is compressed at the parent node. In periodic data collection, the data collected by individual sensors can be considered as a time series. The amount of correlation between time series of two sensory data streams may not be constant over time. Most existing works in this direction do not consider the temporal effect of correlation among data streams generated by periodic sensing. Moreover, the compression can introduce some imperfection that may affect the reliability of the collected data. In this paper, we address the problem of energy efficient data gathering in WSNs while considering variability of correlation among data streams of neighboring sensors. We propose a bucket approximation based framework named EFFECT (energy efficient framework for compression tree) that produces a compression tree based on the compression ratio of data streams from the neighboring sensors in a given sensor network. We perform experiments on real data sets and show that our framework can produce trees with significantly higher lifetime while reducing the average energy consumption of the sensors by at least 20%.