M. Mendula, S. Khodadadeh, S. S. Bacanli, S. Zehtabian, H. Sheikh, L. Bölöni, D. Turgut, and P. Bellavista

Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project


Cite as:

M. Mendula, S. Khodadadeh, S. S. Bacanli, S. Zehtabian, H. Sheikh, L. Bölöni, D. Turgut, and P. Bellavista. Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project. In Proc. of the 23rd International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM'20), pp. 225–233, November 2020.

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Abstract:

The smart home concept can significantly benefit from predictive models that take proactive management operations on home actuators, based on users' behavior evaluation. In this paper, we use a small-scale physical model, the ScaledHome-2 testbed, to experiment with the evolution of measurements in a suburban home under different environmental scenarios. We start from the observation that, for a home to become smart, in addition to IoT sensors and actuators, we also need a predictive model of how actions taken by inhabitants and home actuators affect the internal environment of the home, reflected in the sensor readings. In this paper, we propose a technique to create such a predictive model through machine learning in various simulated weather scenarios. This paper also contributes to the literature in the field by quantitatively comparing several machine learning algorithms (K-nearest neighbor, regression trees, Support Vector Machine regression, and Long Short Term Memory deep neural networks) in their ability to create accurate and generalizable predictive models for smart homes.

BibTeX:

@inproceedings{Mendula-2020-MSWIM,
  author = "M. Mendula and S. Khodadadeh and S. S. Bacanli and S. Zehtabian and H. Sheikh and L. B{\"o}l{\"o}ni and D. Turgut and P. Bellavista",
  title = "Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project",
  booktitle = "Proc. of the 23rd International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM'20)",
  year = "2020",
  month = "November",
  pages = "225-233",
  location = "Alicante, Spain",
  xxxacceptance = "25\%",
  abstract = {
        The smart home concept can significantly benefit from predictive models that take proactive management operations on home actuators, based on users' behavior evaluation. In this paper, we use a small-scale physical model, the ScaledHome-2 testbed, to experiment with the evolution of measurements in a suburban home under different environmental scenarios. We start from the observation that, for a home to become smart, in addition to IoT sensors and actuators, we also need a predictive model of how actions taken by inhabitants and home actuators affect the internal environment of the home, reflected in the sensor readings. In this paper, we propose a technique to create such a predictive model through machine learning in various simulated weather scenarios. This paper also contributes to the literature in the field by quantitatively comparing several machine learning algorithms (K-nearest neighbor, regression trees, Support Vector Machine regression, and Long Short Term Memory deep neural networks) in their ability to create accurate and generalizable predictive models for smart homes.
  },
}

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