Discovering Semantically Meaningful Places from Pervasive RF-Beacons

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

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

3. Why Finding a Place 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. System Overview App X App Y App Z Place Service Place Signatures Visit History … GPS WiFi GSM

5. Discovering Places from RF-Beacons (Numbers of 10 seconds)

6. Discovering Places from RF-Beacons (Numbers of 10 seconds)

7. Why it can be tricky? • Differentiating new beacons from infrequent beacons during transition • Same place, infrequent beacons • Timely detecting exiting and entering nearby places

8. PlaceSense Designed to discover places by continuously monitoring the radio beacons (e.g., WiFi, Cell tower) • Involves Two Steps: • Entering: Detecting when the radio environment is stabilizing • Exiting: Detecting when the radio environment is changing Entering Exiting Infrequent beacons Exiting Entering • Contributions: • Improving the accuracy of the detected entrance and departure times • Reducing missed visits or erroneously (merged, divided) detected visits

9. 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 Conservative Approach** Rapidly empties previous scan windows when a new beacon is found. (to filter out “hallway beacons”) 1 2 3 If sdepth = 3 Entered a place! ** [05 BeaconPrint] tolerates some scan windows with new beacons instead of rapidly emptying. Stable depth, sdepth, specifies how many stable scan windows must be seen. Hallway beacons

10. Step 2: Sensing Departure Detecting a changing radio environment that indicates a departure from a place. Changing radio environment? Detecting new beacons and missing representative beacons* implies the device is leaving. * Representative Beacons: Focusing on beacons with high response rate Rk,x: response rate of beacon x at place k nk : total scan count since the place was entered Representative beacons does not imply a departure Missing infrequent beacons Hybrid Approach** Missing representative beacons & detecting new beacons ** [05 BeaconPrint] only relies on detecting new beacons. Latecomers

11. More on Sensing Entrance/Departure • A single scan window shouldn’t indicate a departure • Tolerance depth (tdepth) : How many scan windows must be unstable • Prevents infrequent beacons dividing a single visit into multiple visits • But, when visiting closely located places … • Tolerance depth introduces delays on determining the departure. • And potential delays may effect detecting entrance to the subsequent place. • (If the travel time between two places is less than the delay) • Buffering Strategy • Buffers and reuses data used to detect departure to detect the next entrance. • Allows rapidly detecting place entry after quick transitions. Traveling between closely located places

12. Related Work in Place Finding

13. 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 diary deteriorated within the first few days. (~ 5 min)

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

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

16. Experiments – Results by Users * Names are pseudonyms PS: PlaceSense, BP: BeaconPrint, KA: Kang et al. Both Precision and Recall are improved by increasing the number of correct places # Correct # Correct + # Interesting Precision = Recall = # Discovered # Remembered

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

18. Summary • PlaceSense improves discovering and recognizing place visits. • PlaceSense (precision: 89%, recall: 92%) • BeaconPrint (precision: 82%, recall: 65%) • Kang et al. (precision: 30%, recall: 28%) • 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

19. Thanks for your time. Questions? http://www.cs.ucla.edu/~dhjkim dhjkim@cs.ucla.edu

20. Appendix – Discovered Places 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 … 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 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!

21. Appendix – Time Accuracy Motivation PlaceSense Related Work Experiments Summary

22. Appendix - Discovering Places from Coordinates