A DIFF-Based Indoor Positioning System Using Fingerprinting Technique and K-Means Clustering Algorithm

Anuwatkun, Apichon; Sangthong, Jirapat; Sang-Ngern, Sommart

doi:10.1109/jcsse.2019.8864175

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…Anuwatkun et al [20] tried k-means using on Euclidean distance in the feature space for clustering formation in a small environment. Instead of using the RSS values directly, the authors used the strength difference among the APs.…”

Section: Related Workmentioning

confidence: 99%

New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting

Torres-Sospedra¹,

Quezada-Gaibor

Mendoza-Silva

et al. 2020

2020 International Conference on Localization and GNSS (ICL-GNSS)

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Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost -around 40% lower-than the original k-means.

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Section: Related Workmentioning

confidence: 99%

New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting

Torres-Sospedra¹,

Quezada-Gaibor

Mendoza-Silva

et al. 2020

2020 International Conference on Localization and GNSS (ICL-GNSS)

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“…Clustering has been widely used to group fingerprints with similar characteristics into classes [23], [29]. It helps to reduce the search area in the online phase of fingerprinting and the energy consumption in resource-constrained devices (i.e., devices with low energy, storage, and computational resources).…”

Section: Related Workmentioning

confidence: 99%

“…One of the most common clustering methods is k-Means, which has been applied in several studies in order to divide the data set into subdatasets, reduce the computational load in the user's device, and improve positioning accuracy. For instance, Anuwatkun et al [29] combined k-Means clustering with the difference of signal strength (DIFF) method to improve the search time and accuracy in the position estimation. Lee and Lee [30] developed an algorithm to find the best k for k-Means, having the main objective to build an accurate radio map and provide a better position estimation.…”

Section: Related Workmentioning

confidence: 99%

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

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Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈ 7% with respect to fingerprinting with the traditional clustering models.

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“…Because of the excessive amount of RSS data, some studies [28,29] proposed to optimize the management of radio maps using the K-means clustering algorithm before KNN positioning, which effectively shortened the calculation time, to improve the real-time performance of indoor positioning algorithms. The Kmeans algorithm needs to determine the number of clusters as prior information.…”

Section: Clustering Algorithmmentioning

confidence: 99%

Overview of WiFi fingerprinting‐based indoor positioning

2022

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Location‐based services have attracted significant attention since the concept of Industry 4.0 was proposed and the ‘Internet +’ era began owing to its social and commercial value. Many scholars have attempted to introduce machine learning in indoor fingerprint positioning to improve indoor positioning accuracy, enhance system robustness, reduce costs, and improve the performance of indoor positioning methods. A comprehensive overview of indoor positioning technology, its methods, and classifications is provided. Furthermore, a detailed review of the application of WiFi fingerprinting and machine learning methods in indoor positioning is presented, and the advantages and disadvantages of these methods when applied to indoor positioning are analyzed. This study summarizes the difficulties and challenges encountered by indoor positioning and suggests development directions.

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A DIFF-Based Indoor Positioning System Using Fingerprinting Technique and K-Means Clustering Algorithm

Cited by 11 publications

References 8 publications

New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting

New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Overview of WiFi fingerprinting‐based indoor positioning

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