@article{5ac83e960c7c4f4a86f78dadb40def3b,
title = "Experimental Analysis on Weight K-Nearest Neighbor Indoor Fingerprint Positioning",
abstract = "Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is weighted K -nearest neighbor (WKNN) fingerprint which positions a user based on K nearest reference points measured beforehand. The challenge lies in how to configure the value of K to obtain the best positioning accuracy. In this paper, we propose a self-adaptive WKNN (SAWKNN) algorithm with a dynamic K}. By adjusting the value of K based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value K = 1. The performance of the proposed algorithm has been evaluated in real-world experiments.",
keywords = "Fingerprint, WiFi, indoor location, weighted K-nearest neighbor (WKNN)",
author = "Jiusong Hu and Dawei Liu and Zhi Yan and Hongli Liu",
note = "Funding Information: Manuscript received May 11, 2018; revised June 28, 2018; accepted July 31, 2018. Date of publication August 9, 2018; date of current version February 25, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61701417, Grant 61771191, and Grant 61401193, in part by the Natural Science Foundation of Hunan Province under Grant 2017JJ2052, Grant 2018JJ2333, and Grant 2017JJ3275, and in part by CERNET under Grant NGII20161010. (Jiusong Hu and Dawei Liu contributed equally to this work.) (Corresponding authors: Dawei Liu; Hongli Liu.) J. Hu is with the School of Electrical and Information Engineering, Hunan University, Changsha 410006, China, and also with the Department of Computer Science and Software Engineering, Xi{\textquoteright}an Jiaotong–Liverpool University, Suzhou 215123, China (e-mail: hjs2008@hnu.edu.cn). Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2019",
month = feb,
doi = "10.1109/JIOT.2018.2864607",
language = "English",
volume = "6",
pages = "891--897",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "IEEE",
number = "1",
}