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Open AccessArticle

Activity Learning as a Foundation for Security Monitoring in Smart Homes

1
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
2
FutureWei Technologies, Inc., Santa Clara, CA 95050, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Subhas Mukhopadhyay, Hemant Ghayvat and Nagender Kumar Suryadevara
Sensors 2017, 17(4), 737; https://doi.org/10.3390/s17040737
Received: 28 February 2017 / Revised: 28 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Sensors for Home Automation and Security)
Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed. View Full-Text
Keywords: security monitoring; activity learning; anomaly detection; smart home automation security monitoring; activity learning; anomaly detection; smart home automation
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MDPI and ACS Style

Dahmen, J.; Thomas, B.L.; Cook, D.J.; Wang, X. Activity Learning as a Foundation for Security Monitoring in Smart Homes. Sensors 2017, 17, 737.

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