Research on Book Location Algorithm of Library Based on Improved LANDMARC Algorithm

Yue Li (), Shuihai Dou (), Yanping Du (), Zhaohua Wang, Xianyang Su () and Lizhi Peng
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Yue Li: Beijing Institute of Graphic Communication
Shuihai Dou: Beijing Institute of Graphic Communication
Yanping Du: Beijing Institute of Graphic Communication
Zhaohua Wang: Beijing Institute of Graphic Communication
Xianyang Su: Beijing Institute of Graphic Communication
Lizhi Peng: Beijing Institute of Graphic Communication

A chapter in LISS 2022, 2023, pp 177-187 from Springer

Abstract: Abstract For the current problems of cumbersome book data, low accuracy of book positioning and difficulty of readers to find books in traditional libraries, this paper proposes an adaptive k-nearest neighbor LANDMARC positioning algorithm. In the traditional LANDMARC indoor localization algorithm, the number of k reference tags is fixed, which will bring in the information of lousy reference tags and has the problem of low localization accuracy. In this paper, the k-value selection rule for the reference tag number is improved, and the improved LANDMARC algorithm with adaptive optimal k-nearest neighbors is proposed. In order to verify the effectiveness of the adaptive optimal k-nearest neighbor LANDMARC algorithm, the simulation of the improved LANDMARC algorithm before and after the improvement is carried out using MATLAB, and the simulation results show that the optimized LANDMARC algorithm can improve the localization accuracy by at most 25.9%. In order to verify the effectiveness of the adaptive optimal k-nearest neighbor LANDMARC algorithm, MATLAB is used to simulate the traditional LANDMARC algorithm and the improved LANDMARC algorithm. The simulation results show that the optimized LANDMARC algorithm has a maximum improvement of 25.9% in localization accuracy. The reliability of the improved algorithm has been significantly improved, which meets the library positioning requirements, promotes readers’ reading stickiness and improves their reading experience, and helps to promote reading for all better.

Keywords: The library; LANDMARC; Adaptive k-nearest neighbor algorithm; Books positioning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-2625-1_13

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DOI: 10.1007/978-981-99-2625-1_13

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