Evaluation of Indoor Positioning System Based on Difference of Signal Strength Using Fingerprinting Technique and K-Means Clustering Algorithm
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Abstract
This paper presents the evaluation of indoor positioning system based on DIFF parameter using fingerprinting technique and K-Means clustering algorithm. In training process, RSS values at the reference positions are surveyed and sent to server to generate the DIFF-fingerprint. After that, the similarly DIFF values have been grouped by using K-Means and keep them to the database. For testing process, the positions are estimated by using the fingerprinting technique and algorithms that are LS and k-NN algorithm. The results show that the DIFF parameter can be used to improve the accuracy of indoor positioning system. However, the no-clustering case was provided slightly higher accuracy than K-Means clustering case. But, the K-Means clustering case can computed faster than noclustering case in testing process. So, the indoor positioning system based on DIFF parameter using fingerprinting technique and K-Means clustering algorithm case can be suitable applied on the mobile devices that have limited CPU and memory. Moreover, it can be used for the system that require fast
computation in positioning process.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright @2021 Engineering Transactions
Faculty of Engineering and Technology
Mahanakorn University of Technology
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