SeReIn-M: Sensor Relationship Inference in Multi-Resident Smart Homes

Abstract

Modern smart homes comprise a large amount of sensors and actuators. Identifying sensor relationships can contribute to automating operational policies of the actuators. However, in a multi-resident smart home, it is difficult to identify sensor relationships due to a variety of simultaneous sensor events. In this paper, we propose a novel two-step approach, which initially extracts features from time series data generated by sensor events to cluster related sensors, and then identifies how each sensor is related to other sensors revealing their physical proximity and enabling a sensor's ability to participate in multiple sensor groups. Experimental results show that our approach performs well in multi-resident homes even if the sensor events are not equally distributed.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.1109/CCNC51664.2024.10454867

Keywords

Policy generation, sensor groups, sensor proximity, spectral clustering, time series data

Publication Date

1-1-2024

Journal Title

Proceedings IEEE Consumer Communications and Networking Conference Ccnc

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