Empirical evaluation of signal-strength fingerprint positioning in wireless LANs

Milioris, Dimitris; Kriara, Lito; Papakonstantinou, Artemis; Tzagkarakis, George; Tsakalides, Panagiotis; Papadopouli, Maria

doi:10.1145/1868521.1868525

Cited by 29 publications

(23 citation statements)

References 19 publications

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“…Bargh et al (2008) use the Kullback-Leibler (KL) divergence to find the (single) nearest neighbour in the space of multinomial counts of Bluetooth dongles. Milioris et al (2010) also perform nearest neighbour matching by resorting to KL divergence, this time on RSSI from WiFi data, but they assume that the RSSI from multiple APs is simply a multivariate Gaussian, a hypothesis that is not always true, as pointed out in Section 1.1. Alternatively, Del Mundo et al (2011) reported that Support Vector Machines (SVMs) with Gaussian or polynomial kernels could achieve better WiFi location classification accuracy than Naive Bayes or nearest neighbours.…”

Section: Prior Art In Probability-based Indoor Localisationmentioning

confidence: 99%

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Probability kernel regression for WiFi localisation

Mirowski¹,

Whiting²,

Steck³

et al. 2012

Journal of Location Based Services

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Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the Received Signal Strength Indication (RSSI) of WiFi for different locations can achieve tracking accuracies of the order of a few meters. RSSI fingerprinting suffers though from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex (e.g bimodal) distributions of WiFi signals, precluding algorithms based on the mean RSSI. Mirowski et al. (2011) have recently introduced a simple methodology that takes into account the full distribution for computing similarities among fingerprints using Kullback-Leibler divergence, and then performs localisation through kernel regression. Their algorithm provides a natural way of smoothing over time and motion trajectories and can be applied directly to histograms of WiFi connections to access points, ignoring RSSI distributions, hence removing the need for fingerprint recalibration. It has been shown to outperform nearest neighbours or Kalman and particle filters, achieving up to 1m accuracy in office environments. In this paper, we focus on the relevance of Gaussian or non-Gaussian distributions for modeling RSSI distributions by considering additional probabilistic kernels for comparing Gaussian distributions and by evaluating them on three contrasting datasets. We discuss their limitations and formulate how the KL-divergence kernel regression algorithm bridges the gap with other WiFi localisation algorithms, notably Bayesian networks, SVMs and K nearest neighbours. Finally, we revisit the assumptions on the fingerprint maps and overview practical WiFi localisation software implementation.

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Section: Prior Art In Probability-based Indoor Localisationmentioning

confidence: 99%

“…Let us denote by N (µ p , Σ p ) and N (µ q , Σ q ) the two multivariate normal distributions p and q that will be fitted to some RSSI measurements coming from J access points. Then, as stated in (Milioris et al, 2010), the Kullback-Leibler divergence between these two Gaussians can be written as (Eq. 2).…”

Section: 41mentioning

confidence: 99%

Probability kernel regression for WiFi localisation

Mirowski¹,

Whiting²,

Steck³

et al. 2012

Journal of Location Based Services

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“…In the offline site survey phase, the service provider collects WiFi signal strengths from multiple access points (APs) at every location of an interested area. Next, in the online operating phase, a to-be-localized client measures the signal strengths at a specific location from nearby APs, and then algorithms such as k-nearest neighbors [1,3] or probability-based algorithms [5] are employed to infer the user's location based on the measured WiFi signal strengths.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving crowdsourced site survey in WiFi fingerprint-based localization

Sun

2016

J Wireless Com Network

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Typically, site survey is an inevitable phase for WiFi fingerprint-based localization which is regarded as one of the most promising techniques for indoor localization. However, the site survey can cause potential location privacy leakage for the participants who contribute their WiFi fingerprint measurements. In this paper, we propose a privacy-preserving site survey scheme for WiFi fingerprint-based localization. In the proposed scheme, we use homomorphic encryption to protect the location privacy of the participants which get involved in the site survey. Further, we employ differential privacy model to ensure that the released data will not breach an individual's location privacy regardless of whether she is present or absent in the site survey group. We theoretically analyze the security of the proposed scheme and use simulation experiments on a real-world data to validate the efficiency of the proposed scheme.

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“…The fingerprint-based techniques consist of two distinct phases. First, during a training phase a wireless device that listens to a channel receives beacons sent periodically by APs and records their RSS values at known positions of the physical space [1]. In a subsequent runtime phase the system also records the RSS values from received beacons but at random unknown positions.…”

Section: Introductionmentioning

confidence: 99%

Building complete training maps for indoor location estimation

Milioris

Bradonjic

Mühlethaler

2015

2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Abstract-Indoor location estimation is a significant task for many ubiquitous and pervasive computing applications, with numerous solutions based on IEEE802.11, bluetooth, ultrasound and infrared technologies. Most of these techniques use the fingerprint-based approach, which needs exhaustive collection of the received signal strengths in various positions of the physical space. In the present work, we exploit the spatial correlation structure of the fingerprints and use the framework of Matrix Completion to build complete training maps from a small number of random sample fingerprints. The experimental evaluation with real data presents the localization accuracy based on complete reconstructed training maps, without making an exhaustive collection of fingerprints.

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Empirical evaluation of signal-strength fingerprint positioning in wireless LANs

Cited by 29 publications

References 19 publications

Probability kernel regression for WiFi localisation

Probability kernel regression for WiFi localisation

Privacy-preserving crowdsourced site survey in WiFi fingerprint-based localization

Building complete training maps for indoor location estimation

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