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The BikeNet Mobile Sensing System for Cyclist Experience Mapping Shane B. Eisenman**, Emiliano Miluzzo*, Nicholas D. Lane* Ron A. Peterson*, Gahng-Seop.

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Presentation on theme: "The BikeNet Mobile Sensing System for Cyclist Experience Mapping Shane B. Eisenman**, Emiliano Miluzzo*, Nicholas D. Lane* Ron A. Peterson*, Gahng-Seop."— Presentation transcript:

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

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

4 Recreational Sensing: Cyclist Experience Mapping 57 million cyclists in the U.S. A diversity of requirements BikeNet BikeNet 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 Air Quality CoastingNoise Distance Braking Car Density Cyclist Community Cyclist Experience Mapping Personal Sensing

6 The Sensing System BikeNet Physical Bike Area Network (BAN)

7 The Sensing System BikeNet Logical Bike Area Network (BAN)

8 The Sensing System BikeNet Simplifying the prototype

9 The Sensing System BikeNet Hardware Prototypes

10 The Sensing System BikeNet 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 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 BANs Hanover, NH USA

13 The Sensing System BikeNet BANs

14 The Sensing System BikeNet Sensor Access Points (SAPs)

15 The Sensing System BikeNet Backend Services

16 The Sensing System BikeNet Tasking

17 The Sensing System BikeNet Sensing

18 The Sensing System BikeNet Delivery

19 The Sensing System BikeNet Presentation + Sharing

20 Sensor Data! BikeNet 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

22 Performance Index BikeNet Distance Duration Speed Path Slope Coasting

23 Performance Inputs: Slope and Coasting BikeNet

24 Health Index BikeNet

25 Health Index BikeNet Noise C0 2 Level Traffic Density

26 Health Input: Car Density BikeNet

27 Health Input: C0 2 Level BikeNet

28 BikeView: Present and Share BikeNet

29 Public Utility Sensing: CO 2 Map ~ Hanover NH BikeNet

30 Lessons BikeNet 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 Debugging on the go!

32 Lessons BikeNet 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 Sometimes it takes 190 odd kilometers to get it right

34 Lessons BikeNet 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 Ground-Truth Validation Helmet

36 Related Projects BikeNet 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 BikeNet Platform for experimentation with mobile sensing systems supporting: Personal Sensing Sharing sensor data within Social Networks Public Utility Sensing

38 BikeNet Cheers for listening Sponsors


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