Integrated location fingerprinting and physical neighborhood for WLAN probabilistic localization

Zhou, Mu; Zhang, Qiao; Tian, Zengshan; Qiu, Feng; Wu, Qi

doi:10.1109/icccnt.2014.6963028

Cited by 12 publications

(3 citation statements)

References 17 publications

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“…Since the development of RADAR, the pioneer IPS based on k-NN (Bahl & Padmanabhan, 2000), many similar systems have been proposed. Although other advanced techniques have also been used to elaborate IPSs based on signal fingerprinting (Bayesian Inference (Zhou et al, 2014b), Neural Networks (Kuo et al, 2013;Campos et al, 2014), Decision Trees (Yim, 2008), Random Forest (Calderoni et al, 2015), among others), we concentrate our work on k-NN based techniques in this paper due to its presence in the literature…”

Section: Related Workmentioning

confidence: 99%

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

Torres-Sospedra

Montoliu

Trilles

et al. 2015

Expert Systems with Applications

239

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Recent advances in Indoor Positioning Systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on Machine Learning and Expert Systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for Received Signal Strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data.There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding.In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrates that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sørensen distance and the powed data representation, the error in determining the place (building and floor) Preprint submitted to ElsevierNovember 23, 2015 has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 meters with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrates that thresholdingshould not be applied in multi-building and multi-floor environments

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Section: Related Workmentioning

confidence: 99%

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

Torres-Sospedra

Montoliu

Trilles

et al. 2015

Expert Systems with Applications

239

150

View full text Add to dashboard Cite

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“…In the two kinds of methods above, the second steps are the process of calculating the locations of the unknown nodes, which are usually adopted by expert systems with machine learning techniques. For example, Patwari et al [8] and Fang et al [14] use maximum likelihood estimation (MLE) and Kamol and Prashant [11] Zheng et al [12] and Feng et al [13] employ k-nearest neighbour and the Euclidean distance to estimate the locations of the unknown nodes, besides, decision trees [15], Bayesian inference [16] and support vector regression [17] are also implemented for the second steps of indoor localisation. The study on the utilisation of neural network for localisation purposes has been made by several research teams [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Multi‐layer neural network for received signal strength‐based indoor localisation

Dai

Ying

2016

IET Communications

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“…The location of a device is commonly determined by computing the distance or the similarity between a fingerprint collected by the device and the fingerprints contained in the reference database [49]. Wi-Fi fingerprinting is a complex subject which can profit from well-established Expert System techniques by implementing advanced machine learning techniques (Bayesian Inference [72], Neural Networks [37,10], Decision Trees [69], and Random Forest [9], among others).…”

Section: Introductionmentioning

confidence: 99%

A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Torres-Sospedra

Moreira

Knauth

et al. 2017

AIS

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Abstract. This paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL-ETRI Indoor Localization Competition which was part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Competitors designed and validated their algorithms against the publicly available UJIIndoorLoc database which contains a huge reference-and validation data set. All competing systems were evaluated using the mean error in positioning, with penalties, using a private test dataset. The authors believe that this is the first work in which Wi-Fi fingerprinting algorithm results delivered by several independent and competing teams are fairly compared under the same evaluation conditions. The analysis also comprises a combined approach: Results indicate that the competing systems where complementary, since an ensemble that combines three competing methods reported the overall best results.

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Integrated location fingerprinting and physical neighborhood for WLAN probabilistic localization

Cited by 12 publications

References 17 publications

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

Multi‐layer neural network for received signal strength‐based indoor localisation

A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

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