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.
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 = "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.
},
}
Generated by bib2html.pl (written by Patrick Riley, Lotzi Boloni ) on Sun Mar 03, 2024 18:41:16