Anomalous occupancy sensor behavior detection in connected indoor lighting systems

Giulia Violatto, Ashish Pandharipande, Shuai Li, Luca Schenato

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)

Samenvatting

We consider the problem of classifying anomalous occupancy sensor behavior in connected indoor lighting systems. Anomalous occupancy sensor behavior may occur in the form of either a high number of false alarms (type-1 anomalies) or missed detection (type-2 anomalies). We consider a supervised machine learning approach to determine whether the detection signal of an occupancy sensor is normal, or exhibits type-1 or type-2 anomalies. We devise occupancy signal features in the time and frequency domains and employ a random forest classifier to perform 3-class classification. The proposed method is evaluated using motion sensor data from an office building, and is shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k-means method and a random forest classifier with a single signal energy feature.

Originele taal-2Engels
TitelIEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's335-339
Aantal pagina's5
ISBN van elektronische versie978-1-5386-4980-0
DOI's
StatusGepubliceerd - 1 apr 2019
Evenement5th IEEE World Forum on Internet of Things, WF-IoT 2019 - Limerick, Ierland
Duur: 15 apr 201918 apr 2019

Congres

Congres5th IEEE World Forum on Internet of Things, WF-IoT 2019
Land/RegioIerland
StadLimerick
Periode15/04/1918/04/19

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