dc.contributor.author |
Osifeko, MO
|
|
dc.contributor.author |
Hancke, GP
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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|
dc.date.accessioned |
2022-01-24T07:48:00Z |
|
dc.date.available |
2022-01-24T07:48:00Z |
|
dc.date.issued |
2021-11 |
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dc.identifier.citation |
Osifeko, M., Hancke, G. & Abu-Mahfouz, A.M. 2021. SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks. <i>IEEE Sensors Journal, 21(22).</i> http://hdl.handle.net/10204/12236 |
en_ZA |
dc.identifier.issn |
1530-437X |
|
dc.identifier.issn |
1558-1748 |
|
dc.identifier.uri |
DOI: 10.1109/JSEN.2021.3103016
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/12236
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|
dc.description.abstract |
The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process. However, the isolated setup of existing surveillance systems makes ADM inefficient and susceptible to staleness due to the lack of resource sharing and continuous learning (CL). CL is the incremental development of models that adapts continuously to the external world. Thus, for efficient CL in surveillance systems, devices must share resources and cooperate with neighbor sites. Yet, solutions from the literature focus on the isolated environment thereby neglecting the need for resource sharing and CL. To address this gap, this paper proposes a cooperative surveillance system called SurveilNet that allows for resource sharing between surveillance sites under the control of a cooperator node. We further propose a lightweight subscription scheme that allows for a joint specialized model development process that continually adapts to the dynamics of the secured environment. Our proposed scheme offers the ability to learn from the neighboring site’s data without compromising data privacy. The performance of our scheme is evaluated using a reclassified UCF-Crime dataset with the result showing the efficiency of our proposed scheme when compared to the state-of-the-art. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9508397 |
en_US |
dc.source |
IEEE Sensors Journal, 21(22) |
en_US |
dc.subject |
Anomaly detection systems |
en_US |
dc.subject |
Federated deep learning |
en_US |
dc.subject |
Internet of Things |
en_US |
dc.subject |
IoT |
en_US |
dc.subject |
Surveillance systems |
en_US |
dc.title |
SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
25293-25306 |
en_US |
dc.description.note |
© 2021 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Osifeko, M., Hancke, G., & Abu-Mahfouz, A. M. (2021). SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks. <i>IEEE Sensors Journal, 21(22)</i>, http://hdl.handle.net/10204/12236 |
en_ZA |
dc.identifier.chicagocitation |
Osifeko, MO, GP Hancke, and Adnan MI Abu-Mahfouz "SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks." <i>IEEE Sensors Journal, 21(22)</i> (2021) http://hdl.handle.net/10204/12236 |
en_ZA |
dc.identifier.vancouvercitation |
Osifeko M, Hancke G, Abu-Mahfouz AM. SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks. IEEE Sensors Journal, 21(22). 2021; http://hdl.handle.net/10204/12236. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Osifeko, MO
AU - Hancke, GP
AU - Abu-Mahfouz, Adnan MI
AB - The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process. However, the isolated setup of existing surveillance systems makes ADM inefficient and susceptible to staleness due to the lack of resource sharing and continuous learning (CL). CL is the incremental development of models that adapts continuously to the external world. Thus, for efficient CL in surveillance systems, devices must share resources and cooperate with neighbor sites. Yet, solutions from the literature focus on the isolated environment thereby neglecting the need for resource sharing and CL. To address this gap, this paper proposes a cooperative surveillance system called SurveilNet that allows for resource sharing between surveillance sites under the control of a cooperator node. We further propose a lightweight subscription scheme that allows for a joint specialized model development process that continually adapts to the dynamics of the secured environment. Our proposed scheme offers the ability to learn from the neighboring site’s data without compromising data privacy. The performance of our scheme is evaluated using a reclassified UCF-Crime dataset with the result showing the efficiency of our proposed scheme when compared to the state-of-the-art.
DA - 2021-11
DB - ResearchSpace
DP - CSIR
J1 - IEEE Sensors Journal, 21(22)
KW - Anomaly detection systems
KW - Federated deep learning
KW - Internet of Things
KW - IoT
KW - Surveillance systems
LK - https://researchspace.csir.co.za
PY - 2021
SM - 1530-437X
SM - 1558-1748
T1 - SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks
TI - SurveilNet: A lightweight anomaly detection system for cooperative IoT surveillance networks
UR - http://hdl.handle.net/10204/12236
ER -
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en_ZA |
dc.identifier.worklist |
25267 |
en_US |