Practical Metropolitan-Scale Positioning for GSM Phones

Chen, Mike Y.; Sohn, Timothy; Chmelev, Dmitri; Haehnel, Dirk; Hightower, Jeffrey; Hughes, Jeff; LaMarca, Anthony; Potter, Fred; Smith, Ian; Varshavsky, Alex

doi:10.1007/11853565_14

Cited by 203 publications

(142 citation statements)

References 15 publications

Supporting

Mentioning

133

Contrasting

Unclassified

Order By: Relevance

“…Note that instead of storing all fingerprint maps on the phone, the phone may be able to simply download them upon entering a building. The building may be identified by either using a GPS receiver if one is available on the phone or through a GSM-based wide-area localization system [5].…”

Section: Performance Evaluationmentioning

confidence: 99%

Accurate GSM Indoor Localization

Otsason

Varshavsky

LaMarca

et al. 2005

Lecture Notes in Computer Science

Self Cite

326

207

View full text Add to dashboard Cite

Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median within floor accuracy of 4 m in large buildings and is able to identify the floor correctly in up to 60% of the cases and is within 2 floors in up to 98% of the cases in tall multi-floor buildings. We report evaluation results of two case studies conducted over a course of several years, with data collected from 6 buildings in 3 cities across North America. The key idea that makes accurate GSM-based indoor localization possible is the use of wide signalstrength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but are too weak to be used for efficient communication. We further show that selecting a subset of highly relevant channels for fingerprinting matching out of all available channels, further improves the localization accuracy.

show abstract

Section: Performance Evaluationmentioning

confidence: 99%

Accurate GSM Indoor Localization

Otsason

Varshavsky

LaMarca

et al. 2005

Lecture Notes in Computer Science

Self Cite

326

207

View full text Add to dashboard Cite

show abstract

“…As it has been previously noticed [3], using weak signal strengths tends to make more harm than good. Our approach, when used to exchange information among users connected to different networks allows to gather a larger number of strong signals, thus potentially leading to better results.…”

Section: Introductionmentioning

confidence: 65%

“…Some works have also explored the use of this approach with the existing cellular networks [3,15]. In contrast with Wi-Fi networks that are only available in limited areas, cellular networks tend to be available everywhere.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Cellular-Based Location System

Navalho

Preguiça

2010

Euro-Par 2010 - Parallel Processing

View full text Add to dashboard Cite

Abstract. A large number of techniques for location estimation relying on the existing wireless radio infrastructures have been proposed in the last few years. Solutions relying on the use of cellular networks can be used almost everywhere, but tend to have low precision. In this paper, we propose the use of information sharing among nearby mobile phones for improving accuracy of cellular-based location. Our information sharing solution was developed in the context of UCLS, a modular location system. This mechanism allows a mobile phone to use additional signal-strength information obtained from nearby devices, that can be connected to different cells of the same or of a different operator. Results show that this additional information helps improving results, with accuracy over 94.8% when using only 2 mobile phones connected to different cell towers when locating the user at the room-level.

show abstract

“…Chen et al (2006)). Within indoor environments, the need for extensive dedicated hardware and repeated physical surveys of the space also deters real-world applications; buildings may not necessarily be accessible and can be subject to constant alterations on their layout.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised labelling of sequential data for location identification in indoor environments

Perez

Pinchin

Brown

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets.Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

show abstract

Practical Metropolitan-Scale Positioning for GSM Phones

Cited by 203 publications

References 15 publications

Accurate GSM Indoor Localization

Accurate GSM Indoor Localization

Collaborative Cellular-Based Location System

Unsupervised labelling of sequential data for location identification in indoor environments

Contact Info

Product

Resources

About