Citation

Chong, Alvin Ming Song and Yeo, Boon Chin and Lim, Way Soong (2022) Automatic data acquisition system for wi-fi fingerprint-based indoor positioning system. International Journal of Innovative Computing, Information and Control (IJICIC), 18 (1). pp. 231-252. ISSN 1349-418X

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Abstract

Indoor Positioning System (IPS) can be developed by using different wireless technologies with different positioning techniques. One of the popular areas of study is the Wi-Fi fingerprinting based IPS because it has high flexibility. However, the implementation of a fingerprint based IPS required obtaining a fingerprint database beforehand. Manually performing data acquisition to obtain a fingerprint database requires a signif-icant amount of time and workforce. Thus, this paper presented a method to perform data acquisition automatically by utilizing the tiled floor characteristic. Furthermore, the fingerprint database constructed was utilized to perform studies on the impact of fingerprint patterns and errors in the location tag of fingerprints on the positioning accuracy of neural networks. Studies show that neural network models presented smaller positioning errors when fingerprints used are with environmental reading formed by RSS from more Wi-Fi anchors. Studies also show that when the location tag of fingerprints used for neural network models training consists of errors, positioning accuracy when validated with fingerprints in the validation set (errorless) depends on the type of error. The location tag of fingerprints used for training suffering large mean errors will significantly impact validation positioning accuracy compared with large standard deviation errors.

Item Type:	Article
Uncontrolled Keywords:	Image processing, fingerprint, Indoor Positioning System (IPS), Line of Sight (LOS)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Feb 2022 02:06
Last Modified:	03 Feb 2022 02:06
URII:	http://shdl.mmu.edu.my/id/eprint/9950

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