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About this paper
Mohammad Rasras, Iuliana Marin, Serban Radu
A multi-agent system prototype simulates a smart home environment, autonomously managing daily activities to optimize energy use and monitor resident health parameters such as sleep and toileting frequency. This system achieves energy savings through agent-controlled devices and utilizes machine learning (KNN and KMC) to identify potential health abnormalities from behavioral data.
Key insights
- Multi-agent systems provide an effective, non-intrusive framework for integrating complex context-aware functionalities in smart homes, addressing both energy and health needs.
- Analyzing repetitive resident actions allows for the determination of behavioral patterns crucial for dynamic energy efficiency and proactive health monitoring.
- Machine learning techniques, including both supervised (KNN) and unsupervised (KMC) methods, are viable for interpreting behavioral data to identify potential health deviations.
Summary by alphaXiv.
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