Matteo Mendula, Siavash Khodadadeh, Salih Safa Bacanl\i, Sharare Zehtabian, Hassam Ullah Sheikh, Ladislau Bölöni, Damla Turgut, and Paolo Bellavista. Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project. In to be presented at the 23rd International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM-2020), November 2020.
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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.
@inproceedings{Mendula-2020-MSWIM, author = "Matteo Mendula and Siavash Khodadadeh and Salih Safa Bacanl\i and Sharare Zehtabian and Hassam Ullah Sheikh and Ladislau B{\"o}l{\"o}ni and Damla Turgut and Paolo Bellavista", title = "Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project", booktitle = "to be presented at the 23rd International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM-2020)", year = "2020", month = "November", 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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