Presentation on theme: "SURROUNDSENSE Mobile Phone Localization via Ambience Fingerprinting."— Presentation transcript:
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 Ambience fingerprinting – the use of sound, color, light, and movement to uniquely identify a logical location
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”
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!
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
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
Sound Fingerprinting & Filtering Sound profile at 100 discrete amplitude values normalized by the total number of samples taken (8000 per-second)
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
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
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
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)
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
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