Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.

Research based on indoor location systems has recently been developed due to growing interest in locationaware services to be implemented in light mobile devices. Most of this work is based on received signal strength (RSS) from access points. However, a major drawback from using RSS is its variability due to indoor multipath effect caused by reflection, diffraction and scattering of signal propagation. Therefore, different device orientations in a fixed location provide significant and different RSS values. In this paper, we propose to extend fingerprinting with device orientation information. Implementation of our location system is based on data mining techniques employing decision tree algorithms. Experimental results demonstrate that using RSS samples with the device orientation information improves the location estimation with high accuracy.

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