Fingerprint-free tracking with dynamic enhanced field division

Zhang, Qingquan; Zhou, Ziqiao; Xu, Wei; Jing, Qi; Guo, Chenxi; Yi, Ping; Zhu, Ting; Xiao, Shuangjiu

doi:10.1109/infocom.2015.7218671

Cited by 19 publications

(7 citation statements)

References 22 publications

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“…In this way, the constraints over the target rule out the dispersed nearest neighbors in signal space with high similarity. Similar region intersection or division scheme has also been reported in some sensor network localization systems like [73] and [74].…”

Section: B Exploiting Spatial and Temporal Signal Patternssupporting

confidence: 72%

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Chan

2016

IEEE Commun. Surv. Tutorials

1,103

639

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The growing commercial interest in indoor locationbased services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of GPS (global positioning system) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-ofsight measurement of access points (APs) and achieves high applicability in complex indoor environment.This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors.Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Section: B Exploiting Spatial and Temporal Signal Patternssupporting

confidence: 72%

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Chan

2016

IEEE Commun. Surv. Tutorials

1,103

639

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“…The model based approaches use the collected RSS fingerprints to train the parameters for the predefined propagation models [14], [33], [51], [52]. These techniques assume a prior path loss model for the indoor propagation which is a logarithmic decay function of the distance from the APs as [53] P L = P L 0 + 10γlog 10 d d 0 (2) where P L is the path loss measured in dB, d is the length of the path, d 0 is the reference distance, and γ is the path loss parameter.…”

Section: A Indoor Localization Approachesmentioning

confidence: 99%

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Khalajmehrabadi

Gatsis

Akopian

2017

IEEE Commun. Surv. Tutorials

289

176

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Abstract-Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.

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“…There are also approaches that use a complex algorithm to reduce noises [9][10][11]; however, most of these approaches require a large quantity of RSS observations at the same location. Some approaches use a lightweight machine learning method to generate limited location fingerprints [12], or variations of fingerprints such as RSS differences between every pair of APs [13], or do not need to generate location fingerprints [14]; however, they suffer from relatively low localization accuracy.…”

Section: Reducing Noises and Generating Fingerprintsmentioning

confidence: 99%

Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

Dong

Ling

Xia

et al. 2017

Wireless Communications and Mobile Computing

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The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.

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Fingerprint-free tracking with dynamic enhanced field division

Cited by 19 publications

References 22 publications

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

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