Footstep and vehicle detection using seismic sensors in wireless sensor network: Field tests
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. © 2013 Gökhan Koç and Korkut Yegin.
| Yazar |
Koç, G. Yegin, K. |
| Yayın Türü | Article |
| Tek Biçim Adres | https://hdl.handle.net/20.500.11831/3290 |
| Koleksiyonlar |
Araştırma Çıktıları | Ön Baskı | WoS | Scopus | TR-Dizin | PubMed 02- WoS İndeksli Yayınlar Koleksiyonu 03- Scopus İndeksli Yayınlar Koleksiyonu |
| Dergi Adı | International Journal of Distributed Sensor Networks |
| Cild | 2013 |
| Sayfalar | - |
| Yayın Tarihi | 2013 |
| Eser Adı [dc.title] | Footstep and vehicle detection using seismic sensors in wireless sensor network: Field tests |
| Yazar [dc.contributor.author] | Koç, G. |
| Yazar [dc.contributor.author] | Yegin, K. |
| Yayın Türü [dc.type] | article |
| Özet [dc.description.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. © 2013 Gökhan Koç and Korkut Yegin. |
| Kayıt Giriş Tarihi [dc.date.accessioned] | 2020-03-18 |
| Yayın Tarihi [dc.date.issued] | 2013 |
| Açık Erişim Tarihi [dc.date.available] | 2020-03-18 |
| Dil [dc.language.iso] | eng |
| Haklar [dc.rights] | info:eu-repo/semantics/openAccess |
| ISSN [dc.identifier.issn] | 15501329 |
| Dergi Adı [dc.relation.journal] | International Journal of Distributed Sensor Networks |
| Cild [dc.identifier.volume] | 2013 |
| Tek Biçim Adres [dc.identifier.uri] | https://hdl.handle.net/20.500.11831/3290 |
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