Footstep and Vehicle Detection Using Seismic Sensors in Wireless Sensor Network: Field Tests

Gökhan Koç,Korkut Yegin

doi:10.1155/2013/120386

Abstract

Extensive field tests were carried out to assess the performance of adaptive thresholds algorithm for footstep and vehicle detection using seismic sensors. Each seismic sensor unit is equipped with wireless sensor node to communicate critical data to sensor gateway. Results from 92 different test configurations were analyzed in terms of detection and classification. Hit and false alarm rates of classification algorithm were formed, and detection ranges were determined based on these results. Amplification values of low-intensity seismic data were also taken into account in the analysis. Algorithm-dependent constants such as adaptive thresholds sample sizes were examined for performance. Detection and classification of seismic signals due to footstep, rain, or vehicle were successfully performed.

Highlights

Seismic sensors are invaluable parts of security systems that focus on perimeter or compound security
There were false vibration type detections, but these false detections were filtered in the classification
False, and missed detection for various QAT memory sizes as a function of range are shown in Figures 8, 9, and 10

Summary

Introduction

Seismic sensors are invaluable parts of security systems that focus on perimeter or compound security. Many detection algorithms have been proposed in the past, but some of them place too much burden on computational resources and some of them are too complex to be implemented on a wireless sensor network, and yet some of them lack field tests. Field tests compromise of footsteps and vehicles at different ranges of the sensors. Another critical aspect of footstep detection is the amount of analog signal gain that is applied to seismic data. Low amplification is ideal for suppressing and identifying noise but has limited application range [10, 11]. Seismic data after being processed at the node, has been transferred to other wireless nodes or directly to the gateway to signal alarm conditions.

Test Setup

Detection Probability and Classification Performance

Result

Algorithm-Dependent Parameters

Findings

Conclusion