Reducing the Data Transmission in Sensor Networks through Kruskal-Wallis Model

Data reduction is one of the most attractive way to conserve the limited energy resources of wireless sensor networks (WSNs). It aims to remove unnecessary data transmission. Therefore, data prediction and reduction mechanisms must be deployed at the source node in order to eliminate the redundant sensed data before sending them to the sink. In this paper, an energy efficient periodic distributed data reduction technique is proposed. Our technique allows each sensor node to search the variation between readings collected at each period based on the Kruskal-Wallis model. Then, the sensor selects a set of representative readings instead of sending the whole readings collected during a period to the sink. To evaluate the performance of our technique, simulations on a publicly available real sensor data followed by experiments in a real-world telosB sensor network testbed have been performed. Compared to other existing approaches, we are able to achieve up to 80% communication reduction while maintaining a high level of data accuracy.