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Experiences with Performance Tradeoffs in Practical, Continuous Indoor Localization

Experiences with Performance Tradeoffs in Practical, Continuous Indoor Localization Azeem J. Khan, Viskash Ranjan , Trung-Tuan Luong , Rajesh Balan and Archan Misra School of Information Systems Singapore Management University. Introduction & Motivation.

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Experiences with Performance Tradeoffs in Practical, Continuous Indoor Localization

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  1. Experiences with Performance Tradeoffs in Practical, Continuous Indoor Localization Azeem J. Khan, ViskashRanjan, Trung-Tuan Luong, Rajesh Balan and Archan Misra School of Information Systems Singapore Management University

  2. Introduction & Motivation • Indoor localization: intensive research has focused on indoor localization Wi-Fi fingerprinting indoor localization • Motivation: • Support all mobile devices, • Energy efficient

  3. Research Questions & Contributions Research Questions Contributions • Localization algorithms: Android and iOS • Study the impact of building characteristics • Reveal the performance limitations of existing controller-based solutions Server-based location tracking solution Characteristics of the indoor environment Performance limitations by existing commercial Wi-Fi infrastructure

  4. Buildings Characteristics

  5. Data Collection Process • Client-based (Android): • Query rate: 20Hz • Server-based (all smart-phones): • Query rate: 1Hz • Data collection: • 6 sets of reading: 3 different times of the day on 2 different days • 4 orientation facing

  6. Localization algorithm Step 1: Location estimate using RADAR Step 2: Path Smoothing using Viterbi

  7. Client-based (Android) vs. Server-based (iOS) • Client based is much better than server-based • Dense deployment of APs in SIS: compare to Mall • Better server-based performance

  8. Server-based limitations • Stale SNR report • Query throughput bottleneck

  9. Issues in Practical Deployment Occupant Density SIS Mall Different fingerprint map at different occupant density Impact on fingerprint data Higher density  larger RSSI variation

  10. Issues in Practical Deployment Occupant Density Impact on indoor localization performance Higher occupant density  greater signal fluctuation  worse performance

  11. Issues in Practical Deployment Energy vs. Accuracy Inertial sensor ( accelerometer and magnetometer) dominate the energy usage  attractive for intermittent sensing but can pose unacceptable energy overhead when executed continuously

  12. Summary and Future work • Building a continuous indoor location tracking system: • Scalable: number of participant and mobile OS platforms • Empirical experiment shows that localization accuracy depends on: building layout, density of deployed APs, occupant density in the environment • Limitation: query controller throughput • Dynamic fingerprint map • Future work:

  13. Questions? Thank You.

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