Residential House Occupancy Detection: Trust-based Scheme Using Economic and Privacy-aware Sensors
Jun Jiang, Chenli Wang, Thomas Roth, Cuong Nguyen, Patrick Kamongi, Hohyun Lee, Yuhong Liu
Internet of Things (IoT) technologies (e.g., power- efficient occupancy-based energy management systems) are increasingly deployed in commercial buildings to reduce building energy consumption. However, the sensors involved in such systems are rarely adopted in residential houses due to their relatively high costs and users' privacy concerns. Low-cost and non-intrusive IoT sensors have been proposed for residential houses for use with machine learning algorithms. Furthermore, such sensors may be triggered very infrequently due to their non- intrusive nature, and it can take several days/weeks to collect sufficient training data. There is a research gap in accurately detecting occupancy information in residential houses with limited training data. This paper proposes a trust-based occupancy detection scheme, which achieves high detection accuracy based on limited training data collected by non-intrusive, low-cost sensors. First, rather than directly taking raw sensor data as inputs, the semantic meanings (i.e., human activity sequences) are extracted from the data based on the order of triggered sensors. Second, the extracted human activity sequences are fed into the proposed trust-based sequence matching scheme for further occupancy detection. Comprehensive experimental results show that, when compared to existing occupancy detection algorithms, the proposed scheme can reliably achieve higher accuracy, especially when only limited training data is available.
, Wang, C.
, Roth, T.
, Nguyen, C.
, Kamongi, P.
, Lee, H.
and Liu, Y.
Residential House Occupancy Detection: Trust-based Scheme Using Economic and Privacy-aware Sensors, IEEE Internet of Things Journal, [online], https://doi.org/10.1109/JIOT.2021.3091098, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932472
(Accessed September 29, 2022)