Abstract
Authentication is the process by which a user establishes his identification when accessing a service. The use of password to identify the user has been a successful technique in conventional computers. However, in pervasive computing where computing resources exist everywhere, it is necessary to perform user identification through various means. This paper addresses the inhabitant identification issue in smart houses. It studies the optimum time and sensor set required to unobtrusively detect the house occupant. We use a supervised learning approach to address this issue by learning Support Vector Machines classifier (SVM), which predict the users by their daily life habits. We have analyzed the early morning routine with six users. From the very first minute, users can be recognized with an accuracy of more than 85%. Then we have applied an SVM feature selection algorithm to remove noisy and outlier features. Thus, this increases the accuracy to 88% using less then 10 sensors.
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Kadouche, R., Pigot, H., Abdulrazaka, B., Giroux, S. (2010). Support Vector Machines for Inhabitant Identification in Smart Houses. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds) Ubiquitous Intelligence and Computing. UIC 2010. Lecture Notes in Computer Science, vol 6406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16355-5_9
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DOI: https://doi.org/10.1007/978-3-642-16355-5_9
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