Localization Using Radial Basis Function Networks and Signal Strength Fingerprints in WLAN

Laoudias, Christos; Kemppi, Paul; Panayiotou, Christos G.

doi:10.1109/glocom.2009.5425278

Cited by 66 publications

(27 citation statements)

References 10 publications

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“…In positioning radial basis function was used for indoor localization in [13] where RSS fingerprints are fitted with corresponding positions, mathematical formulation of interpolation is given by:…”

Section: Multidimensional Data Interpolationmentioning

confidence: 99%

Crowdsourcing based terminal positioning using multidimensional data clustering and interpolation

Boujnah¹,

Korbel²

2016

Annals of Computer Science and Information Systems

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Abstract-Recent years can be characterized by the rapid increase of mobile device usage in people's lives. Contemporary mobile devices are equipped with many sensors and have high computational and processing capabilities. In a crowdsourcing systems, mobile users participate constructively in specific information handling. Data collected by crowd of mobile devices and stored in a database may help offering new services such as operator's radio quality evaluation and tracking of users. In this paper, we focus on mobile positioning by clustering and interpolating data to match fingerprints to positions. Our method is trained during the offline phase and parameters are periodically updated to track possible changes in propagation environment.

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Section: Multidimensional Data Interpolationmentioning

confidence: 99%

Crowdsourcing based terminal positioning using multidimensional data clustering and interpolation

Boujnah¹,

Korbel²

2016

Annals of Computer Science and Information Systems

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“…Such methods utilize a number of RSS fingerprints collected a priori at some predefined reference locations. Location can then be estimated by finding the best match between the currently measured fingerprint and reference fingerprints [1]- [4]. So far, the focus has been on improving accuracy, however the time required to estimate user location is also very important, because it affects the battery consumption of mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Fingerprint-Based Positioning

Laoudias

Michaelides

Panayiotou

2011

2011 IEEE International Conference on Communications (ICC)

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The increasing demand for indoor location-based services has motivated the development of positioning methods that exploit the existing wireless network infrastructure. Accuracy is an important requirement, however fault tolerance is also highly desirable in case of failures or malicious attacks. We investigate the fault tolerance of fingerprint-based methods under a variety of fault or attack scenarios. We study the Subtract on Negative Add on Positive (SNAP) algorithm and modify it appropriately for the WLAN setup. Our results indicate that SNAP achieves adequate accuracy with very low computational complexity and exhibits smoother performance degradation in the presence of faults compared to other methods.

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“…and select any of the available positioning methods from the Algorithms configuration panel, which includes the deterministic K-Nearest Neighbor (KNN) and Weighted K-Nearest Neighbor (WKNN) algorithms [1], as well as the probabilistic Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE) algorithms [19]. The Airplace platform additionally supports two state-of-the-art methods developed in-house, namely the Radial Basis Function (RBF) networks [12] and the Subtract on Negative Add on Positive (SNAP) [13] approaches.…”

Section: Airplace Rss Logger Applicationmentioning

confidence: 99%

Towards planet-scale localization on smartphones with a partial radiomap

Konstantinidis

Chatzimiloudis

Laoudias

et al. 2012

Proceedings of the 4th ACM International Workshop on Hot Topics in Planet-Scale Measurement

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The majority of smartphone localization systems use Assisted-GPS for fine-grained localization in outdoor spaces or WiFibased RSS (Received Signal Strength) technologies for coarsegrain positioning in indoor and outdoor spaces. The former consumes precious energy from mobile devices, is strictly affected by the environment (e.g., cloudy day, forests, etc.) and does not work in indoor spaces. The latter collects RSS from WiFi beams within a user's vicinity and transfers an RSS vector to the server for localization, in which the position of the user is disclosed possibly violating users' privacy. In this paper, we present BloomMap, an innovative and efficient algorithm that conducts a localization process without unveiling the user's location to the localization service, minimizing the energy consumption of the mobile unit and also minimizing the network traffic by not transferring large positioning structures to the client (i.e., known as radiomap). Our framework is designed for planet-scale RSS localization scenarios, which are expected to emerge in the near-future. In particular, a user may localize itself using a subset of a vast data repository of RSS signals that is updated in real time by smartphone wardrivers. Our preliminary evaluation shows that our propositions can localize a device without unveiling its location in approximately 80% less time, energy and network resources than competitive approaches. We also describe our WiFi-based prototype system developed on the Android OS.

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Localization Using Radial Basis Function Networks and Signal Strength Fingerprints in WLAN

Cited by 66 publications

References 10 publications

Crowdsourcing based terminal positioning using multidimensional data clustering and interpolation

Crowdsourcing based terminal positioning using multidimensional data clustering and interpolation

Fault Tolerant Fingerprint-Based Positioning

Towards planet-scale localization on smartphones with a partial radiomap

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