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SurroundSense

SurroundSense. Mobile Phone Localization via Ambience Fingerprinting. Terms. Logical location (usu.) – the demarcated space of a structure; primarily the physical location of a business, e.g., a Starbucks or Wal-Mart

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SurroundSense

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  1. SurroundSense Mobile Phone Localization via Ambience Fingerprinting

  2. Terms • Logical location (usu.) – the demarcated space of a structure; primarily the physical location of a business, e.g., a Starbucks or Wal-Mart • Ambience fingerprinting – the use of sound, color, light, and movement to uniquely identify a logical location

  3. Methodology • Phone handles pre-processing sensor data, sent to server for filtering and matching against pre-populated ambience fingerprints database • Pipelined approach • Partnered design – GSM employed to greatly limit the initial list of candidate locations • Utilizes “smooth” data sets for comparison. • Reduces anomalies • Fingerprint profiles are condensed into “impressions”

  4. Proposal

  5. Advantages • Scalability, accuracy • GPS/GSM/WiFi are prone to errors when distances between logical locations are close (e.g., a shared wall) • GSM/WiFi aren't always present, e.g., in developing countries – not scalable to add hardware to every locality!

  6. Assumptions • Relies on the notion of competitive uniqueness – a company has an economic incentive to maintain a unique ambience • Phones are typically out-of-pocket

  7. WiFiFingerprinting & Filtering • Fingerprint used for filtering; however, used for matching in the absence of color/light matching • Phone records AP MAC addresses every 5 seconds • Uses the frequency of MAC addresses from nearby APs to compute fingerprint

  8. Sound Fingerprinting & Filtering • Sound profile at 100 discrete amplitude values normalized by the total number of samples taken (8000 per-second)

  9. Motion Fingerprinting & Filtering • Used a trained pattern recognition tool to analyze user motion in different stores • Tool divided movement into two states: moving and static • Moving vs. static ratios were computed and classified into three different buckets

  10. Color/Light Fingerprinting & Matching • Pictures taken of floor • Floors are less prone to pattern variations, which require more sophisticated processing • Floors are still very diverse, thus still good candidates for SurroundSense'sobjectives • Partially alleviates privacy concerns • Employs Hue, Saturation, and Lightness to distinguish the floor colors, how many of each color, and the intensity of the ambient light

  11. Accuracy • Correct matching of ambient fingerprints occurred 87% of the time (average) • WiFi-only trials were accurate 70% of the time (average) • 80% of users showed an accuracy > 80% • Limited trial using Indian shops had a 100% match rate

  12. Limitations • Energy usage of SurroundSense hasn’t been studied • Locations of low ambience-diversity bring down successful match percentage (airports and office buildings) • Motion trace occasionally takes a lot of time (waiting for a table in a restaurant, for instance)

  13. Future Work • Substitute motion trace with compass and Bluetooth readings when motion tracing requires a lot of time to converge • Grouping of like businesses • Allow user to select correct location from a set of close matches • SurroundSensewill train itself based on the selection

  14. Questions? • This is where text would go if this weren’t a questions slide.

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