Introducing a decision tree-based indoor positioning technique

Yim, Jaegeol

doi:10.1016/j.eswa.2006.12.028

Cited by 112 publications

(96 citation statements)

References 28 publications

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“…These differentiated stages are usually referred to as offline and online phases and some examples of this work include Kaemarungsi & Krishnamurthy (2004); Brunato & Battiti (2005); Roos et al (2002); Youssef et al (2003);Yim (2008). Alternative approaches based on triangulation and trilateration (Bahl & Padmanabhan (2000); Krishnan et al (2004); Peterson et al (1998);Li et al (2000)) study physical signal propagations.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised labelling of sequential data for location identification in indoor environments

Perez

Pinchin

Brown

et al. 2016

Expert Systems with Applications

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In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets.Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

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Section: Related Workmentioning

confidence: 99%

Unsupervised labelling of sequential data for location identification in indoor environments

Perez

Pinchin

Brown

et al. 2016

Expert Systems with Applications

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“…Kushki [65] used the distance calculation method of kernel function and AP selection strategy. The average positioning error could be kept at approximately 2m.…”

Section: (4) Other Deterministic Positioning Algorithmsmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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“…In addition, this can indicate the performance of the algorithm. In this work, we consider three well-known positioning algorithms with different learning method: Decision Tree [16], k-Nearest Neighbors [17] and Artificial Neural Network [18]. They have their advantage for learning data.…”

Section: Fingerprinting-based Indoor Positioningmentioning

confidence: 99%

The Novel Performance Evaluation Method of the Fingerprinting-Based Indoor Positioning

Premchaisawatt

Ruangchaijatupon

2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The kNearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.

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Introducing a decision tree-based indoor positioning technique

Cited by 112 publications

References 28 publications

Unsupervised labelling of sequential data for location identification in indoor environments

Unsupervised labelling of sequential data for location identification in indoor environments

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

The Novel Performance Evaluation Method of the Fingerprinting-Based Indoor Positioning

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