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Discovering Semantically Meaningful Places from Pervasive RF-Beacons

Discovering Semantically Meaningful Places from Pervasive RF-Beacons. Outline . Introduction Geometry-based vs Fingerprint-based Place Learning The PlaceSense Algorithm Experiment Data Collection Implementation Evaluation Metrics Results. Introduction . Place learning algorithm :

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Discovering Semantically Meaningful Places from Pervasive RF-Beacons

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  1. Discovering Semantically Meaningful Placesfrom Pervasive RF-Beacons

  2. Outline • Introduction • Geometry-based vsFingerprint-based Place Learning • The PlaceSense Algorithm • Experiment • Data Collection • Implementation • Evaluation Metrics • Results

  3. Introduction • Place learning algorithm : • Important to a individual user and carries a semantic meaning • Input : sequence of time-series sensor data • Output: sequence of tuples (date,entertime, leave time, place name)

  4. Geometry-based vs Fingerprint-based • Geometry-based(GPS): • Time-based clustering: compare the distance between the mean of the current cluster and the new measurement against the distance threshold • Fingerprint-based(WIFI): • BeaconPrint: using multiple scan windows to distinguish beacons, compares the fingerprint seen by the device to a list of place fingerprints learned by the system

  5. The PlaceSense Algorithm • continuously monitoring the radio beacons in the environment ( wifi AP or cell tower ID) around a mobile device.

  6. Entrance to a place: • Window size : W • Stable depth: s: 0~smax • smax × w

  7. Departure from a place: • representative beacon • responsive rate: rep • Tolerance depth: t: 0~tmax

  8. Buffering strategy • Rapidly detect place entry after quick transitions. • PlaceSense buffers overlapping data and starts entry determination in parallel

  9. Experiment • human participants to log any place they visited and stayed for more than five minutes

  10. Data Collection • Nokia N95 mobile phone • integrated GPS and built-in Wi-Fi. • Sampling rate : 0.1hz • Data collectors were asked to stay at a place for a predefined amount of time • ground-truth: each data collector was asked to keep a diary of the name and time they entered and left every place they stayed more than 5 minutes during the experiment

  11. Evaluation Metrics

  12. Implementation • time-based clustering algorithm • Beaconprint: • PlaceSense: smax=3, tmax=3

  13. Results

  14. Results

  15. Results

  16. conclusion • It uses response rate to select representative beacons and suppresses the influence of infrequent beacons • PlaceSenses accuracy gains are particularly noticeable in challenging radio environments where beacons are inconsistent and coarse • PlaceSense is accurate at discovering places visited for short durations

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