A Distributed Processing Technique for Sensor Data Applied to Underwater Sensor Networks

Data reduction is a well known efficient technique to reduce energy consumption in wireless sensor networks (WSN). It consists in reducing the amount of data sensed and transmitted to the sink. In this paper, we propose an energy-efficient two-levels data reduction technique based on a clustering architecture. At the first level, each sensor sends a set of representative points to the cluster-head (CH) at each period, instead of sending the raw data. When data points are received by the CH, it uses the Euclidean distance in order to eliminate redundant data generated by neighboring sensor nodes, before sending them to the sink. To validate our approach, we applied our technique on real underwater sensor data and we compared them with other existing data reduction methods. The results show the effectiveness of our technique in terms of improving the energy consumption and the network lifetime, without loss in data fidelity.