Abstract
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.
Original language | English |
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Title of host publication | IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 335-339 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-4980-0 |
DOIs | |
Publication status | Published - 1 Apr 2019 |
Event | 5th IEEE World Forum on Internet of Things, WF-IoT 2019 - Limerick, Ireland Duration: 15 Apr 2019 → 18 Apr 2019 |
Conference
Conference | 5th IEEE World Forum on Internet of Things, WF-IoT 2019 |
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Country/Territory | Ireland |
City | Limerick |
Period | 15/04/19 → 18/04/19 |
Keywords
- Connected lighting
- Occupancy sensors
- Random forest classifier