Data-driven Context and Preference Discovery in Smart Environment
Date Issued
2015
Date
2015
Author(s)
Chiang, Tsung-Chi
Abstract
As we are moving towards the Internet of Things (IoT), a significant number of heterogeneous sensors deployed around the word. The amounts of Big Data are contin-uously generated from those sensors in activity daily living (ADL). One of the applica-tions in IoT is smart home that proactively provides context-aware services by recog-nizing user activities among sensory data. In the other word, activity recognition (AR) plays a key component to add value to raw sensor data we need to understand it. Collec-tion, modeling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. However, for AR to build personalization activity daily liv-ing for each user and overcome the human labeling effort in Big Data, it is important to exploit unsupervised way from activity discovery (AD) [2], which is finding unknown patterns directly from low-level sensor data without any pre-defined assumptions. There are three major contributions in this thesis. Firstly, a novel nonparametric model of activity discovery is proposed to reduce the effort of data annotation while the analysis of Big Data is being performed. Secondly, we propose a service discovery en-gine to learning user preference in an unsupervised way. Thirdly, a decision engine for proactively providing suitable service is proposed. We combine both the discovered user activity and corresponding service preference to improve the suitability of service with smart environment.
Subjects
Smart Home
Activity Recognition
Activity Discovery
Unsupervised Learning
Type
thesis
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