[01] Shi Chen, School of Computer Science and Technology, Nanjing University of Technology, Nanjing, China; School of Information Engineering, Yancheng Teachers University, Yancheng, China.

From mobile internet to the Internet of Things, location is a fundamental and indispensable information. People put forward an urgent need for accurate location information, and various location services also provide great convenience for people's daily life. As the outdoor positioning technology is quite mature, people focus on indoor positioning and propose a series of indoor positioning technologies and algorithms. Based on fingerprint localization, this paper proposes an indoor localization system based on the improved weighted K-Nearest neighbour (WKNN) algorithm. In the offline phase, we deploy Bluetooth access points (APs) and filter out APs with strong signals to reduce computing costs and improve localization accuracy. We process the AP signal data after screening and build a fingerprint database. In the online localization stage, we propose a WKNN algorithm based on weighted distance. This algorithm can effectively reduce the influence of signal fluctuations and improve the accuracy of online localization. At the same time, AP screening can effectively reduce the real-time localization time and improve localization efficiency. We collected a large amount of experimental data from the basement of the community near the school. Experiments show that our method can not only reduce computational complexity, but also effectively improve localization accuracy and improve real-time localization efficiency.

Indoor Localization, WKNN, Wireless Network, Fingerprint Technology

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