TY - GEN
T1 - A Comparative Study of Bluetooth Indoor Positioning Using RSS and Machine Learning Algorithms
AU - Wu, Chunxiang
AU - Wang, Yapeng
AU - Ke, Wei
AU - Yang, Xu
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s)
PY - 2023/12/14
Y1 - 2023/12/14
N2 - This paper presents a comparative study that explores the use of Bluetooth technology for indoor positioning by measuring Received Signal Strength (RSS). It assesses how effective this technology is by employing three machine learning algorithms: K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), and Convolutional Neural Network (CNN). The research begins with an analysis of the results obtained from field tests, following by some general insights in order to improving the accuracy of the positioning system. These positioning algorithms have shown moderate effectiveness, indicating potential for future business applications. The study is based on experimental data collected using beacons in a controlled indoor environment, along with the three algorithms. Importantly, all the algorithms perform well in our indoor testing setup, achieving an average accuracy of less than 2 meters. Notably, the CNN algorithm outperforms the others, achieving an impressive accuracy of 1.28 meters.
AB - This paper presents a comparative study that explores the use of Bluetooth technology for indoor positioning by measuring Received Signal Strength (RSS). It assesses how effective this technology is by employing three machine learning algorithms: K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), and Convolutional Neural Network (CNN). The research begins with an analysis of the results obtained from field tests, following by some general insights in order to improving the accuracy of the positioning system. These positioning algorithms have shown moderate effectiveness, indicating potential for future business applications. The study is based on experimental data collected using beacons in a controlled indoor environment, along with the three algorithms. Importantly, all the algorithms perform well in our indoor testing setup, achieving an average accuracy of less than 2 meters. Notably, the CNN algorithm outperforms the others, achieving an impressive accuracy of 1.28 meters.
KW - Bluetooth low energy
KW - Convolutional neural network
KW - Indoor positioning
KW - K-Nearest neighbor
KW - Received signal Strength
UR - http://www.scopus.com/inward/record.url?scp=85192163205&partnerID=8YFLogxK
U2 - 10.1145/3638884.3638960
DO - 10.1145/3638884.3638960
M3 - Conference contribution
AN - SCOPUS:85192163205
T3 - ACM International Conference Proceeding Series
SP - 478
EP - 483
BT - ICCIP 2023 - 2023 the 9th International Conference on Communication and Information Processing
PB - Association for Computing Machinery
T2 - 9th International Conference on Communication and Information Processing, ICCIP 2023
Y2 - 14 December 2023 through 16 December 2023
ER -