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Indoor Localization and Navigation for Pervasive and Sensor-Based Computing Environment

Indoor Localization and Navigation for Pervasive and Sensor-Based Computing Environment. Widyawan. Electrical Engineering and Information Technology Department Gadjah Mada University. Agenda. Vision of Pervasive Computing Indoor Localization Fingerprinting-based Indoor Localization

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Indoor Localization and Navigation for Pervasive and Sensor-Based Computing Environment

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  1. Indoor Localization and Navigation for Pervasive and Sensor-Based Computing Environment Widyawan Electrical Engineering and Information Technology Department GadjahMada University

  2. Agenda • Vision of Pervasive Computing • Indoor Localization • Fingerprinting-based Indoor Localization • Particle Filter algorithm • Pedestrian Dead Reckoning • Challenges Remain

  3. Old Paradigm • For over forty years, computation has centered about machines, not people. We have catered to expensive computers, pampering them in air-conditioned rooms or carrying them around with us. Purporting to serve us, they have actually forced us to serve them …. [MIT Oxygen Project]

  4. Vision of Pervasive Computing • In the future, computation will be human-centered. • It will be available everywhere but invisible (embedded sensors) • Post-desktop [Mark Weiser, 1988]

  5. Features and Application • Features: • Transparent interfaces: gesture recognition, speed recognition • Context-aware: location and time • Example of Applications: • Print this document to Mr. Risanuri • Follow me GUI • Smart Building • Knowing user location is key … !

  6. Localization Technologies

  7. Localization Sensors Ultrasound Bluetooth WSN GPS RFID UWB

  8. Methods Trilateration Multilateration Fingerprint Dead Reckoning

  9. Applications Healthcare Industrial : Car Manufacture Retail Logistik www.ubiaware.com

  10. Fingerprint and Conventional Algorithms • Pattern recognition algorithm: • kNN • ANN • But, it is not enough ! • Random noise • Non Gaussian system • Non linear filtering is needed

  11. Bayesian Filtering Xt-1 Xt Xt+1 zt-1 zt Zt+1 Hidden Markov Model

  12. Particle Filter for Localization motion model a posteriori distribution att=1 a posteriori distribution at t =0 measurement model

  13. Measurement Model For incorporating sensor measurement into the particle filter Based on 3 types of dissimilarities Fingerprint

  14. Motion Model & Map Filtering

  15. Test-bed Experiment [Video]

  16. Dead Reckoning • Widely Used in

  17. Sensors

  18. Tools to be Used

  19. Applications • Pedestrian Dead Reckoning

  20. Detecting the Steps

  21. Particle Filtering for Dead Reckoning [Video]

  22. Challenges and Opportunities • The ubiquity of accurate, low-powered sensors • The ‘killer’ applications • Energy saving, assisted living, office/productivity, convenience ? • Standards and interoperability • Privacy and Security • Internet of things • The RTLS market will grow from $153 Million in 2009 to $2.6 Billion in 2018

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