A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent y...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 14; p. 6282
Main Authors: Zheng, Weimin, Pang, Senyuan, Liu, Ning, Chai, Qingwei, Xu, Lindong
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 10-07-2023
MDPI
Subjects:
Accuracy
Algorithms
compact
Elitism
Energy consumption
Females
Food
Global positioning systems
GPS
Localization
Males
Mathematical optimization
Optimization algorithms
RSSI localization
Sensors
Simulation
snake optimization
Snakes
WKNN
RSSI localization
compact
WKNN
snake optimization
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Summary:Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore, we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.
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content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23146282