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.