SeReIn: Smart Home Sensor Relationship Inference

Abstract

Determining sensor relationships in smart environments is complex due to the variety and volume of time series information they provide. Moreover, identifying sensor relationships to connect them with actuators is difficult for smart home users who may not have technical experience. Therefore, in this paper, we propose a novel unsupervised learning approach, named SeReIn, to automatically group sensors by their inherent relationships solely using time series data. SeReIn extracts three features from smart home time series data-Frequent Next Event (FNE), Time Delta (TD), and Frequency (FQ), and applies Spectral Clustering to group the related sensors. The application of unsupervised learning enables our approach to operate anywhere in the smart home domain regardless of the sensor types and deployment scenarios. SeReIn functions on both large deployments consisting of around 70 sensors and small deployments of only 10 sensors. Evaluation of SeReIn on real-world smart home datasets has shown that it can recognize inherent spatial relationships in smart homes with a Calinski-Harabasz score of up to 90.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.23919/SoftCOM58365.2023.10271580

Keywords

IoT, relationship inference, sensor, smart home, time series, unsupervised learning

Publication Date

1-1-2023

Journal Title

2023 31st International Conference on Software Telecommunications and Computer Networks Softcom 2023

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