Indoor Positioning Using Adaptive KNN Algorithm Based Fingerprint Technique

Mosleh, Mahmood F.; Abd‐Alhameed, Raed A.; Qasim, Osama A.

doi:10.1007/978-3-030-05195-2_2

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…In deterministic methods, RSSI is usually used as a feature parameter in combination with a deterministic matching algorithm for location estimation. [10][11][12] based on KNN method uses a similarity measure to distinguish between fingerprint data and measurement signals in a dataset, and identifies the target point to be measured as the reference point (RP) in the fingerprint library that is closest to its fingerprint in order to determine the node location. The complexity of this algorithm, although low, while does not applicable to unstable indoor environments with the wide fluctuations of RSSI signals.…”

Section: Related Workmentioning

confidence: 99%

CTSLoc: an indoor localization method based on CNN by using time-series RSSI

et al. 2021

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Due to the complexities of indoor WiFi signal propagations, it is challenging to improvethe performance of indoor fingerprint-based positioning techniques which is the main hot research in Internet of Things (IoT). Most existing methods have limited positioning accuracy, since they do not take the full advantage of the information available, i.e. timing information attached to the Received Signal Strength Indication (RSSI) vector, and adopt the inappropriate training methods. This paper proposes an indoor localization method based on Convolutional Neural Network (CNN) by using time-series RSSI, termed CTSLoc, by taking into account the correlation among RSSI in time and space. A CNN model is used to extract the temporal fluctuation patterns of RSSI and learn the nonlinear mappings from the signal features with time and space to position coordinates. Finally the trained model is used to predict the user's location. An extensive experiment has been carried out in a space with the size of nearly 1000 squared meters, and a comprehensive comparison with several existing methods indicates that CTSLoc attains a lower average localization error (i.e. 4.23m) and more stable performance than those methods. The CTSLoc method performs relatively less dependent on the amount of data which also eliminates spatial ambiguity and reduces the effect of noise on localization.

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Section: Related Workmentioning

confidence: 99%

CTSLoc: an indoor localization method based on CNN by using time-series RSSI

et al. 2021

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“…The authors in study [4] propose a method that utilizes Improved W-KNN to enhance indoor localizat ion performance based on fingerprinting by leveraging the relationship between the nearest fingerprint and (K-1) au xiliary fingerprints to determine the position. In the quest for enhanced adaptability, Adaptive KNN ad justs the number of neighbors (K) based on the local density of data points, dynamically tailoring the algorithm to varying spatial distributions within the indoor environment [5]. The paper introduces an enhanced KNN algorith m featuring a variable K. The fundamental concept revolves around dynamically modify ing the K value according to the discrepancies between measured signals and the corresponding values within the database.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction with Truncated Singular Value Decomposition and K-Nearest Neighbors Regression for Indoor Localization

Thi,

Manh,

Anh

et al. 2023

IJACSA

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Indoor localization presents formidable challenges across diverse sectors, encompassing indoor navigation and asset tracking. In this study, we introduce an inventive indoor localization methodology that combines Truncated S ingular Value Decomposition (Truncated S VD) for dimensionality reduction with the K-Nearest Neighbors Regressor (KNN Regression) for precise position prediction. The central objective of this proposed technique is to mitigate the complexity of highdimensional input data while preserving critical information essential for achieving accurate localization outcomes. To validate the effectiveness of our approach, we conducted an extensive empirical evaluation employing a publicly accessible dataset. This dataset covers a wide spectrum of indoor environments, facilitating a comprehensive assessment. The performance evaluation metrics adopted encompass the Root Mean S quared Error (RMS E) and the Euclidean distance error (ED E)-widely embraced in the field of localization. Importantly, the simulated results demonstrated promising performance, yielding an RMS E of 1.96 meters and an average ED E of 2.23 meters. These results surpass the achievements of prevailing state-of-the-art techniques, which typically attain localization accuracies ranging from 2.5 meters to 2.7 meters using the same dataset. The enhanced accuracy in localization can be attributed to the synergy between Truncate d S VD's dimensionality reduction and the proficiency of KNN Regression in capturing intricate spatial relationships among data points. Our proposed approach highlights its potential to deliver heightened precision in indoor localization outcomes, with immediate relevance to real-time scenarios. Future research endeavors involving comprehensive comparative analyses with advanced techniques hold promise in propelling the field of accurate indoor localization solutions forward.

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