Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN

WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.