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1 Discovering Semantically Meaningful Places from Pervasive RF-Beacons Donnie Kim, Deborah Estrin UCLA Center for Embedded Networked Sensing CENS Urban.

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Presentation on theme: "1 Discovering Semantically Meaningful Places from Pervasive RF-Beacons Donnie Kim, Deborah Estrin UCLA Center for Embedded Networked Sensing CENS Urban."— Presentation transcript:

1 1 Discovering Semantically Meaningful Places from Pervasive RF-Beacons Donnie Kim, Deborah Estrin UCLA Center for Embedded Networked Sensing CENS Urban Sensing is collaborative work of many faculty, staff, and students in partnership with NSF NeTS-FIND, Cisco, Nokia, Schematic, Sun, UCLA REMAP, UCLA ITS, Walt Disney Imagineering R&D Jeffrey Hightower Intel Labs Seattle Ramesh Govindan USC Embedded Networks Laboratory Mobile phones as instruments to understand physical processes in the world

2 2 Places We Go Indoor Places Most of the places we go are indoors. A single building can have multiple places.(e.g., multiplex building, shopping malls, etc.) Visit Frequency Some places are visited more often than others. Outdoor Places Some are outdoors. (e.g., bus stops, tennis courts, plazas, etc.) Visit Duration Some places are visited longer than others.

3 3 Why Finding Places Matters? Location Aware Reminders To-do lists Social Networking Applications Twitter, Facebook, etc. Health management + Intervention Context triggered behavior interventions, self-monitoring Human Spatial and Temporal Behavior Research Data Research for urban planning, architecture, epidemics

4 4 Visit History Place Service Place Signatures GPSWiFiGSM App XApp YApp Z System Overview …

5 5 Discovering Places from RF-Beacons

6 6

7 7 PlaceSense Designed to discover places by continuously monitoring the radio beacons Involves Two Steps: 1.Entering: Detecting when the radio environment is stabilizing 2.Exiting: Detecting when the radio environment is changing Stable Radio Environment? Familiar beacons: if the previous scan windows contained it New beacons: if none of the previous scan windows contained it (e.g., WiFi, Cell tower) EnteringExiting Intermittent beacons Entering Exiting Scan Window: A window size w defines the smallest time unit in which the algorithm will determine entrance/departure to a place (non-overlapping)

8 8 Step 1: Sensing Entrance Continuously seen stable scan windows imply a potential entrance to a place. Stable Scan Window? If a scan window does not contain any new beacons*, its stable. * if none of the previous scan windows contained it Previous Scan Windows? Current scan window is saved and compared against the following scan. Scan windows are accumulated until entrance is determined or a new beacon is found. How many continuous stable scans? Stable depth, s max, specifies how many stable scan windows must be seen. { } Previous Scan Windows Conservative approach** Empties previous scan windows when a new beacon is found. (to filter out “hallway beacons”) Hallway beacons ** [05 Hightower] proposed to tolerate some scan windows with new beacons instead of rapidly emptying.

9 9 Step 2: Sensing Departure Detecting a changing radio environment that indicates a departure from a place. Changing radio environment? Detecting new beacons or missing familiar beacons* implies the device is leaving. * If the previous scan windows contained it Problem: Infrequent Beacons Missing: detected at the beginning but disappears  does not imply a departure Late coming: not detected at the beginning but appears  does not imply a departure Filtering Infrequent Beacons out Representative Beacons: Focusing on beacons with high response rate R k,x : response rate of beacon x at place k n k : total scan count since the place was entered Hybrid approach** Missing representative beacons & detecting new beacons Missing infrequent beacons Latecomers Representative beacons ** [05 Hightower] only relies on detecting new beacons.

10 10 More Perks for Sensing Entrance/Departure Avoiding a single scan window determining a departure Tolerance depth, t max, specifies at least how many scan windows must be unstable. Prevents infrequent beacons dividing a single visit into multiple visits. Visiting closely located places head to head Tolerance depth introduces delays on determining a departure. The delay may effect detecting entrance to the subsequent place. (If the travel time between two places is less than the delay) Traveling between closely located places Buffering Strategy Allows rapidly detecting place entry after quick transitions. Buffers overlapping data and starts entry determination in parallel, as soon as the t value is below t max.

11 11 Place Learning – Two Classes Geometry-basedFingerprint-based Input Location coordinates (e.g., GPS, WiFi/Cell tower triangulation) Radio environment (e.g., currently visible cell towers, WiFi access points) Pros Tightly coupled with the geographical location of the place Does not depend on the underlying positioning system’s accuracy (especially indoors) Cons Depends on the underlying positioning system’s accuracy and availability Radio environment may change over time (affecting recognition not necessarily detection)

12 12 Experiments – Data Collection Mobile Device’s Hardware/Software Nokia N95 mobile phone: integrated GPS and built-in WiFi Campaignr: Software configured to collect GPS/WiFi/GSM traces every 10 seconds Data uploaded to a server every night Data Collection Three data collectors Scripted Tour: for accurate ground-truth (on UCLA campus) Each data collector individually selected 10 places they go often (30 visits for 8,10,15 min) Real-life Data: for further validation Collected 4 week-long trace logs from each collectors as they went about their normal life Ground-truth Each data collector kept a diary of place visits (≥ 5 min) [enter time, leave time, name] Webpage illustrating the GPS coordinates: Provided for reviews/corrections (however, GPS data was not available in most of the indoors) Time accuracy of the dairy deteriorated within the first few days. (~ 5 min)

13 13 Experiments – Evaluation Metrics * [07 Zhou] did not considered merged and divided Four types of erroneous place discovery Remembered Places: recorded by people Discovered Places: found by algorithms Interesting Places: forgotten place visits (+) Correct, Interesting (−) False, Missed, Merged, Divided Precision = # Correct + # Interesting # Discovered Recall = # Correct # Remembered

14 14 Experiments – Results PlaceSense reduces the number of missed places while also increasing the number of interesting and false places. Many indoor places were merged as a single visit

15 15 Experiments – Results by Users AaronBryanChrisAll PSBPKAPSBPKAPSBPKAPSBPKA Cor. 233156812511826324217579726513223 Int. 101261013022924 Mer. 614138231518516151384544461 Div. 22112303612210636 Mis. 050213522836548616797 Fal. 6210142201442134851 Recall 0.970.650.340.900.650.230.910.660.300.920.650.28 Precision 0.950.810.360.870.800.230.880.850.330.890.820.30 Both Precision and Recall is improved by significantly increasing the number of correct places Precision = # Correct + # Interesting # Discovered Recall = # Correct # Remembered PS: PlaceSense, BP: BeaconPrint, KA: Kang et al.* Names are pseudonyms

16 16 Experiments – Does it help recognition? Yes! Significantly improves discovering and recognizing short visits Frequently visited places are often briefly visited

17 17 Summary PlaceSense provides a significant improvement in discovering and recognizing places. PlaceSense (precision: 89%, recall: 92%) BeaconPrint (precision: 82%, recall: 65%) PlaceSense accuracy gains are particularly noticeable in challenging radio environments where beacons are inconsistent and coarse PlaceSense detects entrance/departure time with over twice the precision of previous approaches (thanks to judicious use of buffering and timing) PlaceSense is accurate at discovering places visited for short durations* (less than 30 minutes) or places where the device remains mobile * Valuable to emerging applications like life-logging and social location sharing

18 18 Thanks for your time. Questions? m

19 19 Appendix – Discovered Places Home CENS Eng-IV 14-129B Chevron Vermont BH4404 Chipotle Westwood Fowler A103B Kinsey Pav 1220 MS5200 Borders Westwood Bruin Plaza Target Highland SD Yin-Yin Chinese iMax Regal Wooden Center Famima! Ranch 99 Dublin BH5436 Powell Library Target La Brea Marc Melrose Haines 220 Ralphs Westwood Ackerman Post Office Parking Lot4 Barnes & Novel Westwood BH3276 Verra’s Office Kerkhoff Patio Ackerman Bus stop Ami Restaurant LAX terminal 2 BH3803 Bus stop Home BH4760 Mr. Noodles In-and-out Westwood Yamato Japanese Coffee bean Westwood BH3771 Ackerman Tsunami Trader Joes National Seas Café Ralphs Overland Coffee Bean Galey Tennis court Palms Whole Foods Westwood Starbucks Venice …

20 20 Appendix – Time Accuracy

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