[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2022.09.007

Intelligent LoRa-Based Positioning System

Chen, Jiann-Liang (Department of Electrical Engineering, National Taiwan University of Science and Technology)
Chen, Hsin-Yun (Department of Electrical Engineering, National Taiwan University of Science and Technology)
Ma, Yi-Wei (Department of Electrical Engineering, National Taiwan University of Science and Technology)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.9, 2022 , pp. 2961-2975 More about this Journal

Abstract

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

Keywords

Location-Based Service; LoRa; Fingerprint Mechanism; Machine Learning Algorithm; Synthetic Minority Over-sampling Technique (SMOTE); Density-Based Spatial Clustering of Applications with Noise (DBSCAB);

Citations & Related Records

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