The BikeNet Mobile Sensing System for Cyclist Experience Mapping

1. Shane B. Eisenman**, Emiliano Miluzzo*, Nicholas D. Lane* Ron A. Peterson*, Gahng-Seop Ahn** and Andrew T. Campbell* *Dartmouth College, **Columbia University The BikeNet Mobile Sensing System for Cyclist Experience Mapping

2. Sequence The MetroSense Project & BikeNet The sensing system Sensor data! Lessons Related work Wrap up BikeNet niclane@cs.dartmouth.edu

3. MetroSense • People-centric Sensing • Bringing sensor networks into mainstream use by the general population • Sensing systems applied to everyday activities • BikeNet • Representative of this class of sensing systems • Focused on recreational sensing BikeNet niclane@cs.dartmouth.edu

4. BikeNet • Recreational Sensing: Cyclist Experience Mapping • 57 million cyclists in the U.S. • A diversity of requirements Athletic Training Fun and Leisure Means of Transport BikeNet niclane@cs.dartmouth.edu

5. BikeNet • Demonstrating the faces of people-centric sensing systems: Public Utility Sensing Personal Sensing Social Network Shared Data Air Quality Braking Cyclist Experience Mapping Cyclist Community Noise Coasting Sensing power for the people Car Density Distance BikeNet niclane@cs.dartmouth.edu

6. The Sensing System PhysicalBike Area Network (BAN) BikeNet niclane@cs.dartmouth.edu

7. The Sensing System Logical Bike Area Network (BAN) BikeNet niclane@cs.dartmouth.edu

8. The Sensing System Simplifying the prototype BikeNet niclane@cs.dartmouth.edu

9. The Sensing System Hardware Prototypes BikeNet niclane@cs.dartmouth.edu

10. The Sensing System • Sampling meaningful sensor data required sensor type specific consideration of: • Mounting • Housing • Calibration • Meeting these requirements were as challenging as any part of the system. • Example: • Tilt Sensor BikeNet niclane@cs.dartmouth.edu

11. The Sensing System • Example: Tilt Sensor (slope of path) • Used 2-D Accelerometer • Complicated by: • Noise from bike frame vibration • Difference in precise orientation angle. • Bike specific error characteristics demanding bike specific calibration • 3 point calibration process with known stationary angles BikeNet niclane@cs.dartmouth.edu

12. The Sensing System BANs Hanover, NH USA BikeNet niclane@cs.dartmouth.edu

13. The Sensing System BANs BANs BikeNet niclane@cs.dartmouth.edu

14. The Sensing System Sensor Access Points (SAPs) BikeNet niclane@cs.dartmouth.edu

15. The Sensing System Backend Services BikeNet niclane@cs.dartmouth.edu

16. The Sensing System Tasking BikeNet niclane@cs.dartmouth.edu

17. The Sensing System Sensing BikeNet niclane@cs.dartmouth.edu

18. The Sensing System Delivery BikeNet niclane@cs.dartmouth.edu

19. The Sensing System Presentation + Sharing BikeNet niclane@cs.dartmouth.edu

20. Sensor Data! • Data collection began in the summer of 2006 • Participants included members of the sensor lab and the general public • More than 100 kilometers of data collected • Anonymized traces available soon on Crawdad archive BikeNet niclane@cs.dartmouth.edu

21. Performance Index BikeNet niclane@cs.dartmouth.edu

22. Performance Index Distance Duration Path Slope Coasting Speed BikeNet niclane@cs.dartmouth.edu

23. Performance Inputs:Slope and Coasting BikeNet niclane@cs.dartmouth.edu

24. Health Index BikeNet niclane@cs.dartmouth.edu

25. Health Index Noise Traffic Density C02 Level BikeNet niclane@cs.dartmouth.edu

26. Health Input: Car Density BikeNet niclane@cs.dartmouth.edu

27. Health Input: C02 Level BikeNet niclane@cs.dartmouth.edu

28. BikeView: Present and Share BikeNet niclane@cs.dartmouth.edu

29. Public Utility Sensing: CO2 Map ~ Hanover NH BikeNet niclane@cs.dartmouth.edu

30. Lessons • Mobility and people bring new challenges to experimental system development. • How to debug and perform evaluation? • Experiments require much more time and effort to perform • Experiments are less predictable with people in the loop • Difficulties exist in finding an experimental methodology (i.e., repeatability). BikeNet niclane@cs.dartmouth.edu

31. Lessons Debugging on the go! BikeNet niclane@cs.dartmouth.edu

32. Lessons • Moving from protocols to caring about the payload changes everything! • Noisy data. • Vibrations from the bike frame. • Consider physical solutions (i.e. improving the mounting) before attempting post processing solutions • Validating inferences and collected sensor data requires time and effort. • Counting cars by hand with button clicks from a bike (tricky and dangerous) • Manual measurement of road angles • Ground Truth Helmet BikeNet niclane@cs.dartmouth.edu

33. Lessons Sometimes it takes 190 odd kilometers to get it right BikeNet niclane@cs.dartmouth.edu

34. Lessons • Moving from protocols to caring about the payload changes everything! • Noisy data. Vibration in the bike frame. • Determining appropriate sampling rates. • Consider physical solutions (i.e. improving the mounting) before attempting post processing solutions • Validating inferences and sensor data requires time and effort. • Counting cars by hand with button clicks from a bike (tricky and dangerous) • Manual measurement of road angles • Ground Truth Helmet BikeNet niclane@cs.dartmouth.edu

35. Lessons Ground-Truth Validation Helmet BikeNet niclane@cs.dartmouth.edu

36. Related Projects • Existing Cyclist Systems • Stovepipe commercial solutions • Body Area Networks and Personal Area Networks • SATIRE, MIThrill • DTNs, Mobile Sensing Systems • Haggle, Cartel, ZebraNet • People-Centric Sensing • MIT Media Labs, UCLA, UIUC, Nokia Research, Intel Research, Microsoft Research, Motorola BikeNet niclane@cs.dartmouth.edu

37. Wrap Up • BikeNet • Platform for experimentation with mobile sensing systems supporting: • Personal Sensing • Sharing sensor data within Social Networks • Public Utility Sensing BikeNet niclane@cs.dartmouth.edu

38. Cheers for listening http://bikenet.cs.dartmouth.edu Sponsors BikeNet niclane@cs.dartmouth.edu