| Summary: | To study the intelligent sensing and positioning technology of the Internet of Things (IoT) combined with the K-nearest neighbor algorithm, the K-nearest neighbor matching algorithm and optimization algorithm are introduced using the indoor Wi-Fi positioning technology. The study proposes weighting K-nearest neighbor (WKNN) by weighted Euclidean distance, adaptive weighted Euclidean distance K-nearest neighbor Wi-Fi localization algorithm, and optimal K-value Wi-Fi fingerprint localization algorithm. The experimental error is verified. The experimental results show that the lowest error of continuous acquisition of 3 s signal values in experimental environment A is 1.8815 m, which is 10.13% lower than the error of only acquiring 1 s for the same K-value. The lowest error of environment B scheme two can reach 1.8862, which is 7.06% lower than the error of the same K-value. The optimal K-value Wi-Fi fingerprint positioning algorithm by distance constraint has better positioning accuracy than other KNN positioning algorithms, and the positioning fluctuation is smaller. The average positioning error of the optimal K in environment A is 1.2987 m, which is 0.2797 m less than the average of the traditional positioning algorithm. In environment B, the average positioning error of the optimal K is 1.5353 m, which is 0.3253 m less than the average of the traditional positioning algorithm. Therefore, the optimal K-value Wi-Fi positioning algorithm proposed has better performance.
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