Anomalous occupancy sensor behavior detection in connected indoor lighting systems

Giulia Violatto, Ashish Pandharipande, Shuai Li, Luca Schenato

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

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 languageEnglish
Title of host publicationIEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages335-339
Number of pages5
ISBN (Electronic)978-1-5386-4980-0
DOIs
Publication statusPublished - 1 Apr 2019
Event5th IEEE World Forum on Internet of Things, WF-IoT 2019 - Limerick, Ireland
Duration: 15 Apr 201918 Apr 2019

Conference

Conference5th IEEE World Forum on Internet of Things, WF-IoT 2019
CountryIreland
CityLimerick
Period15/04/1918/04/19

Keywords

  • Connected lighting
  • Occupancy sensors
  • Random forest classifier

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