The analysis of infrasonic signals from Wireless Sensor Networks

Wireless sensor network has already been widely applied in different environmental monitoring include volcano eruption, earthquake detection, flooding detection, structures deformations detection, and chemical hazardous detection. The base station collects the information from the sensor nodes and transforms the collected information into the requested form to cater for different applications. Infrasound is used to monitor big events at large distances. It has noticeable features such as powerful capability of through objects with small attenuation in transmission and may propagate at a long distance. However, the signals'' data collected will have various characteristics because of the different environmental conditions increasing the complexity of data analysis and dropping the accuracy rate of the classification or prediction module. To overcome the challenge, we utilized various feature extraction techniques in both time domain and frequency domain to extract the different properties and then integrate the attributes of the original signals into the complete set of features. In the second stage, we put forward a feature selection method, with Query-Based Attribute Evaluator (QBAE) to reach an effective feature selection, aiming at find out optimum feature sub-set, on the one hand, reduced data dimensions and data volume, on the other hand, enhanced the accuracy of classification and identification and reduced calculation time. Finally, we applied a practical case of detected signals from a volcanic eruption monitoring system for verification, as the experiment results shown, the selected features sub-set with the method we put forward, had better efficacy at classification than traditional methods. And through QBAE feature selection method, in the process of selecting important attributions, the important features of collected infrasonic wave signals of volcanic eruption were found and sufficient to describe the scenario. Thereby, the method we raised is suitable to be applied in the problem area of environment monitoring was verified.