Optimized Indoor Positioning based on WIFI in Mobile Classroom Project

The Mobile Classroom Project aims at building an interactive platform between students and teachers. The LBS(Location Based Services) Teaching Platform is an important part of it. With its help, teachers can send homework, in-class exercises or related learning materials to students, which take mobile teaching equipment in the classroom. Students can submit their homework and in-class test in specified location quickly. The indoor positioning system is the foundation of the LBS teaching platform. However, the current indoor localization based on WIFI(Wireless Fidelity) fingerprint cannot consider both effects of environment to AP(Access Point) and complexity of localization algorithm, leading to the result of low location accuracy or consume more resources of mobile equipment. This paper proposes an optimized Indoor Positioning algorithm based on WIFI. It uses a reliable AP algorithm as AP selection algorithm to reduce the effects of the indoor environment, and takes reliable AP algorithm combining hybrid of KNN(K-Nearest Neighbor) and Bayesian decision into account to obtain better location accuracy and to reduce complexity of positioning computation possibly. In this paper, we realized the indoor positioning system and gave the experiments. We can see the location accuracy is improved and the complexity of the algorithm is reduced.