  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.
