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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

20. Sensor Data! BikeNet niclane@cs.dartmouth.edu 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

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

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

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

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

25. Health Index BikeNet niclane@cs.dartmouth.edu Noise C0 2 Level Traffic Density

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

27. Health Input: C0 2 Level BikeNet niclane@cs.dartmouth.edu

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

29. Public Utility Sensing: CO 2 Map ~ Hanover NH BikeNet niclane@cs.dartmouth.edu

30. Lessons BikeNet niclane@cs.dartmouth.edu 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).

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

32. Lessons BikeNet niclane@cs.dartmouth.edu 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

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

34. Lessons BikeNet niclane@cs.dartmouth.edu 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

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

36. Related Projects BikeNet niclane@cs.dartmouth.edu 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

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

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