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- Publisher Website: 10.1007/978-3-319-60033-8_5
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Conference Paper: Wi-Dog: Monitoring school violence with commodity WiFi devices
Title | Wi-Dog: Monitoring school violence with commodity WiFi devices |
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Authors | |
Keywords | Abnormal activities Physical violence Channel state information Wireless sensing |
Issue Date | 2017 |
Publisher | Springer. |
Citation | 12th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2017), Guilin, China, 19-21 June 2017. In Ma, L, Khreishah, A, Zhang, Y, Yan, M (Eds.), Wireless Algorithms, Systems, and Applications: 12th International Conference, WASA 2017, Guilin, China, June 19-21, 2017, Proceedings, p. 47-59. Cham: Springer, 2017 How to Cite? |
Abstract | Monitoring school violence is critical for the prevention of juvenile delinquency and promotion of social harmony. Pioneering approaches employ always-on-body sensors or cameras with limited surveillance area, which cannot provide ubiquitous violence monitoring. In this paper, we present Wi-Dog, a non-invasive physical violence monitoring scheme based on commodity WiFi infrastructures. The key intuition is that violence-induced WiFi signals convey informative characteristics of intensity, irregularity and continuity. To identify school violence from violence-alike actions (e.g., jump, lie down and run), we develop a precise noise reduction method by selecting sensitive antenna pair and subcarriers. Moreover, a wavelet-entropy-based segmentation method is proposed to detect movement transitions in the distance, and the complete local-global analysis is further adopted to improve overall performance. We implemented Wi-Dog using commercial WiFi devices and evaluated it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog with average detection accuracy of 0.9. |
Persistent Identifier | http://hdl.handle.net/10722/303533 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID | |
Series/Report no. | Lecture Notes in Computer Science ; 10251 Theoretical Computer Science and General Issues ; 10251 |
DC Field | Value | Language |
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dc.contributor.author | Zhou, Qizhen | - |
dc.contributor.author | Wu, Chenshu | - |
dc.contributor.author | Xing, Jianchun | - |
dc.contributor.author | Li, Juelong | - |
dc.contributor.author | Yang, Zheng | - |
dc.contributor.author | Yang, Qiliang | - |
dc.date.accessioned | 2021-09-15T08:25:31Z | - |
dc.date.available | 2021-09-15T08:25:31Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 12th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2017), Guilin, China, 19-21 June 2017. In Ma, L, Khreishah, A, Zhang, Y, Yan, M (Eds.), Wireless Algorithms, Systems, and Applications: 12th International Conference, WASA 2017, Guilin, China, June 19-21, 2017, Proceedings, p. 47-59. Cham: Springer, 2017 | - |
dc.identifier.isbn | 9783319600321 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303533 | - |
dc.description.abstract | Monitoring school violence is critical for the prevention of juvenile delinquency and promotion of social harmony. Pioneering approaches employ always-on-body sensors or cameras with limited surveillance area, which cannot provide ubiquitous violence monitoring. In this paper, we present Wi-Dog, a non-invasive physical violence monitoring scheme based on commodity WiFi infrastructures. The key intuition is that violence-induced WiFi signals convey informative characteristics of intensity, irregularity and continuity. To identify school violence from violence-alike actions (e.g., jump, lie down and run), we develop a precise noise reduction method by selecting sensitive antenna pair and subcarriers. Moreover, a wavelet-entropy-based segmentation method is proposed to detect movement transitions in the distance, and the complete local-global analysis is further adopted to improve overall performance. We implemented Wi-Dog using commercial WiFi devices and evaluated it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog with average detection accuracy of 0.9. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Wireless Algorithms, Systems, and Applications: 12th International Conference, WASA 2017, Guilin, China, June 19-21, 2017, Proceedings | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 10251 | - |
dc.relation.ispartofseries | Theoretical Computer Science and General Issues ; 10251 | - |
dc.subject | Abnormal activities | - |
dc.subject | Physical violence | - |
dc.subject | Channel state information | - |
dc.subject | Wireless sensing | - |
dc.title | Wi-Dog: Monitoring school violence with commodity WiFi devices | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-60033-8_5 | - |
dc.identifier.scopus | eid_2-s2.0-85026356211 | - |
dc.identifier.spage | 47 | - |
dc.identifier.epage | 59 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000446997700005 | - |
dc.publisher.place | Cham | - |