Abstract
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.
Highlights
Location estimation has been a term of growing interest for years as lightweight mobile devices have become the standard in the real world
Indoor location estimation based on multiple weighted decision trees was evaluated on the second floor of an office building at Mannheim University, Germany
We have proposed a low complexity positioning system based on multiple weighted decision trees
Summary
David Sánchez-Rodríguez 1,3, *, Pablo Hernández-Morera 2,3 , José Ma. Quinteiro 2,3 and Itziar Alonso-González 1,3.
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