Data Reduction in Sensor Networks: Performance Evaluation in a Real Environment

Abstract

Data reduction is an effective technique for energy saving in wireless sensor networks. It consists on reducing sensing and transmitting data while conserving a high quality of collected information. In this letter, we propose an online data reduction model based on Kruskal-Wallis test that allows sensor nodes to adapt their sensing rates based on the data variance. Then, we propose a local aggregation algorithm to reduce further the data set size before sending to the sink. Experimentation on real telosB sensor network testbed shows the effectiveness of our approach in reducing the size of data transmitted over the network and thus saving energy.