| Autor: |
Jiann-Liang Chen, Hsin-Yun Chen, Yi-Wei Ma |
| Předmět: |
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| Zdroj: |
KSII Transactions on Internet & Information Systems; Sep2022, Vol. 16 Issue 9, p2961-2975, 15p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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