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Indoor Localization Based on Optimized KNN

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  • Xuanyu Zhu

Abstract

In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the “Navigator” of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.

Suggested Citation

  • Xuanyu Zhu, 2021. "Indoor Localization Based on Optimized KNN," Network and Communication Technologies, Canadian Center of Science and Education, vol. 5(2), pages 1-34, December.
    • Handle: RePEc:ibn:nctjnl:v:5:y:2021:i:2:p:34
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    References listed on IDEAS

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    1. Wei Xie & Jie-sheng Wang & Cheng Xing & Sha-Sha Guo & Meng-wei Guo & Ling-feng Zhu, 2020. "Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm," Complexity, Hindawi, vol. 2020, pages 1-26, May.
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      JEL classification:

      • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
      • Z0 - Other Special Topics - - General

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