Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity

Campos, Rafael Saraiva; Lovisolo, Lisandro; Campos, Marcello L. R. de

doi:10.1016/j.eswa.2014.04.011

Cited by 78 publications

(56 citation statements)

References 24 publications

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“…Authors in [151] employ unsupervised clustering to allow the collected fingerprints to group freely in the signal strength space, without precluding -through the imposition of architectural constraints -any natural arrangement of the collected fingerprints. This is combined with majority voting committees of backpropagation artificial neural networks to deliver floor detection rates between 91% and 97%.…”

Section: B Floor Identification With Wlanmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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Section: B Floor Identification With Wlanmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

View full text Add to dashboard Cite

show abstract

“…The pseudo-code of LocalReliefF-C is displayed in Figure 1. The method firstly calculates the weight for each AP (Lines 1-12), then sorts the weights in a descending order and, finally, retains the N of the APs in the set Sb that correspond to the N highest weights (Lines [13][14]. Afterwards, it traverses the set Sb to calculate the correlation coefficients for every two APs and obtains the final set of best-discriminating APs after removing the redundant ones (Lines 15-22).…”

Section: Proposed Localrelieff-c Ap Selection Methodsmentioning

confidence: 99%

“…The deterministic algorithm stores the RSS value from each AP in the fingerprint database, whereas the probabilistic algorithm stores the RSS probability distribution model. The probabilistic algorithms are generally believed to be more effective because of their capability of dealing with the time variation of RSS [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection

Song

Wang

Yuan

2016

IJGI

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Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C( a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption.

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“…By his experiment he showed that by using CSI the accuracy can be enhanced. Rafael Saraiva Campos [4] focused on improving the accuracy in multi-floor indoor environment which include floor identification, in his paper the positioning of Wi-Fi nodes are shown. The effects of proposed solution on Database Correlation Methods are shown experimentally by using actual measured data.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Geo Fencing and Mobile Sensing: Comparison Analysis

Iqbal

Hassan²

2018

ijarcs

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Abstract:In current scenario mobile phones are one of the most important part of our life, it is not only used for just making calls but also for many different activities these days. With the rapid growth of technologies smart phones also comes with a lot of interesting features such as fingerprint sensors, which can be used for different purpose such as marking attendance of the students as well as employees in schools, colleges, universities and offices. Many different available technologies such as: -Global Positioning System (GPS) for outdoor environment and Wi-Fi for indoor environment can be used to make an attendance system and reduce the chances of proxies in schools and colleges and for tracking students' location. Also if by any means there is chances of proxies then proxy detection is very important, which can be done by using few proxy detection methods.

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Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity

Cited by 78 publications

References 24 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection

Hybrid Geo Fencing and Mobile Sensing: Comparison Analysis

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