@inproceedings{17fb880fc1cd4b7bb974115405f0c993,
title = "A new weighted indoor positioning algorithm based on the physical distance and clustering",
abstract = "The weighted K-nearest neighbor (WKNN) algorithm is one of the most frequently used algorithms for indoor positioning. However, the traditional WKNN algorithm select the k points only based on their received signal strength (RSS), and the algorithm weights the reference points' coordinates by the RSS, which is not accurate enough because of the exponential relationship between RSS and physical distance. Therefore, in order to improve the positioning accuracy of the traditional location algorithm, this paper proposes a new algorithm based on clustering and the physical distance of the RSS. Experiments were conducted in an office building and results demonstrate that the proposed algorithm is better than a series of indoor positioning algorithm. This proposed algorithm is based on the WKNN algorithm and the Kmeans algorithm.",
keywords = "Clustering, Indoor localization, Manhattan distance, Physical distance, Wireless sensors network",
author = "Hao Qin and Shuo Shi and Xiangyu Tong",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 ; Conference date: 24-06-2019 Through 28-06-2019",
year = "2019",
month = jun,
doi = "10.1109/IWCMC.2019.8766365",
language = "英语",
series = "2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "237--242",
booktitle = "2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019",
address = "美国",
}