Open AccessJournal Article
Implementation of a Prototype Positioning System for LBS on U-campus
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TLDR
The Relative-Interpolation Method is proposed to improve the accuracy of outdoor positioning and is faster than the existing indoor positioning methods in the real-time phase.Abstract:
Location-based service is one of the most popular buzzwords in the field of U-cities. Positioning a user is an essential ingredient of a location-based system in a U-city. For outdoor positioning, GPS based practical solutions have been introduced. However, the measurement error of GPS is too big for it to be used for U-campus services, because the size of a campus is smaller than that of a city. We propose the Relative-Interpolation Method to improve the accuracy of outdoor positioning. However, indoor positioning is also necessary for a U-campus because the GPS signal is not available inside buildings. For indoor positioning, various systems including Cricket, Active Badge, and so on have been introduced. These methods require special equipment dedicated to positioning. Our method does not require such equipment because it determines the user's position based on the received signal strength indicators (RSSIs) from access points (AP) which are already installed for WLAN. The algorithm we use for indoor positioning is a kind of fingerprinting method. However, our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, the proposed method is faster than the existing indoor positioning methods in the real-time phase. We integrated our indoor and outdoor positioning methods and implemented a prototype indoor-outdoor positioning system on a laptop. The experimental results are discussed in this paper. In implementing the prototype, we also implemented a C# library function which can be used to read the RSSIs from the APs.read more
Citations
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Smartphone Sensor Value Pattern Analysis with Neural Network
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References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Proceedings ArticleDOI
RADAR: an in-building RF-based user location and tracking system
TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Journal Article
Data Mining Concepts and Techniques
TL;DR: Data mining is the search for new, valuable, and nontrivial information in large volumes of data, a cooperative effort of humans and computers that is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining which produces new, nontrivials information based on the available data set.
Journal ArticleDOI
The active badge location system
TL;DR: A novel system for the location of people in an office environment is described, where members of staff wear badges that transmit signals providing information about their location to a centralized location service, through a network of sensors.
Proceedings ArticleDOI
The Cricket location-support system
TL;DR: The randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy are described.