Spatial Correlation based Data Redundancy Elimination for Data Aggregation in Wireless Sensor Networks

The dense distributed deployment of Wireless Sensor Networks (WSNs) causes the sensors to generate big amount of data which are highly correlated and redundant. Transmitting such spatially correlated and redundant data consumes more sensor energy and consequently decreases the lifetime of network. Eliminating redundancy is thus necessary in the sensed data and also while processing the sensed data. By employing appropriate data aggregation techniques, the data redundancy can be minimized. We leverage statistical techniques in sensor networks and propose a novel Spatial Correlation based Data Redundancy Elimination for Data Aggregation (SCDRE) protocol that eliminates redundancy at two levels: at source level, it uses simple data similarity function and at aggregator level, it uses correlation coefficient to eliminate redundancy and aggregate the data. We have evaluated the proposed protocol in terms of aggregation ratio, data accuracy and energy consumption and the results show that SCDRE outperforms other existing techniques. In addition, SCDRE is more robust in the presence of noise and outliers. By eliminating data redundancy to greater extent, our protocol experiences less communication overhead and significantly enhances the lifetime of sensor networks.