Room Recognition Using Discriminative Ensemble Learning with Hidden Markov Models for Smartphones

Carrera Villacrés, José Luis; Zhao, Zhongliang; Braun, Torsten (22 June 2018). Room Recognition Using Discriminative Ensemble Learning with Hidden Markov Models for Smartphones. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC). Bologna, Italy. 9-12 September 2018.

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An accurate room localization system is a powerful tool for providing location-based services. Considering that people spend most of their time indoors, indoor localization systems are becoming increasingly important in designing smart environments. In this work, we propose an efficient ensemble learning method to provide room level localization in smart buildings. Our proposed localization method achieves high room-level localization accuracy by combining Hidden Markov Models with simple discriminative learning methods. The localization algorithms are designed for a terminal-based system, which consists of commercial smartphones and Wi-Fi access points. We conduct experimental studies to evaluate our system in an office-like indoor environment. Experiment results show that our system can overcome traditional individual machine learning and ensemble learning approaches.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Carrera Villacrés, José Luis; Zhao, Zhongliang and Braun, Torsten

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

Language:

English

Submitter:

Dimitrios Xenakis

Date Deposited:

07 May 2018 09:44

Last Modified:

24 Mar 2021 10:47

BORIS DOI:

10.7892/boris.116376

URI:

https://boris.unibe.ch/id/eprint/116376

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