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. WiFi Fingerprinting & 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's objectives  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  SurroundSense will train itself based on the selection

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