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The BikeNet Mobile Sensing System for Cyclist Experience Mapping Joshua Cox

The BikeNet Mobile Sensing System for Cyclist Experience Mapping Joshua Cox. Authors. Columbia University – Computer Science Shane Eisenman Gahng-Seop Ahn Dartmouth College – Electrical Engineering Emiliano Miluzzo Nicholas Lane Ronald Paterson Andrew Campbell. Previous Work.

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The BikeNet Mobile Sensing System for Cyclist Experience Mapping Joshua Cox

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  1. The BikeNet Mobile Sensing System for Cyclist Experience Mapping Joshua Cox

  2. Authors • Columbia University – Computer Science • Shane Eisenman • Gahng-Seop Ahn • Dartmouth College – Electrical Engineering • Emiliano Miluzzo • Nicholas Lane • Ronald Paterson • Andrew Campbell

  3. Previous Work • SoundSense • The Rise of People-Centric Sensing • SkiScape Sensing • CenceMe • People Centric Inferencing

  4. “An extensible mobile sensing system for cyclist experience mapping leveraging opportunistic sensor networking principles and techniques.” What is BikeNet?

  5. Typical Bike Sensors • Wheel speed • Distance traveled • Calories burned

  6. BikeNet Sensors • Performance/Fitness • wheel speed, pedal speed, distance traveled • path incline, lateral tilt • heart rate, galvanic skin response (stress level) • Environment/Experience • pollution, allergen, and noise levels • roughness of terrain

  7. Inferred Measurements • Calories burned – wheel speed, weight • Traffic level – magnetometer, microphone • Fitness level – pedal speed, wheel speed • Coasting/Braking – path incline, wheel/pedal speed • Health – traffic, pollution, noise 1.0 - a1∗CarDensity - a2∗CO2 Level - a3∗SoundLevel • Performance – wheel/pedal speed, distance, incline b1∗HillAngle + b2∗WheelSpeed/PedalSpeed + b3∗ Distance

  8. BikeNet Features • Disconnected Operation • opportunistic networking paradigm • delay tolerant sensing • real-time sensing • Long Term Performance Trend Analysis • personal repository • public database • spatio-temporal trend analysis

  9. BikeNet Features • Data Collection and Local Presentation • continuous sensing • event-triggered sensing • custom profile preferences • Data Query and Remote Presentation • web-based portal • publish/share data

  10. BikeNet System • Mobile Sensor Tier (BAN) • Sensors (Tmote Invents), phone (Nokia N80) • Bluetooth • SAP Tier • WiFi, GSM • Server Tier • Ethernet-connected servers • Tasking, data storage and visualization

  11. Data Exchange • Tasking exchange • SAP to BAN • What do I sense? • Uploading exchange • BAN to SAP • Here's what I've sensed. • Muling exchange • BAN to BAN • Help me deliver this data.

  12. BikeNet System

  13. Hardware

  14. Hardware

  15. http://bikenet.cs.dartmouth.edu BikeView Demo

  16. Sensor Data

  17. Sensor Data

  18. Sensor Data

  19. Sensor Data

  20. Sensor Data

  21. Social Aspect • Data Sharing • Live Queries Submission • Potential Social Networking Applications • Facebook • Twitter

  22. My Take • Commercial Potential • Flawed Performance Rating • speed • Weather Sensors • wind • humidity • cloudy/sunny • Security/Privacy

  23. Shane B. Eisenman, Emiliano Miluzzo, Nicholas D. Lane, Ronald A. Peterson, Gahng-Seop Ahn, and Andrew T. Campbell, "The BikeNet Mobile Sensing System for Cyclist Experience Mapping", In Proc. of Sensys 2007, Sydney Australia, November 6 - 9th 2007. BikeView - the BikeNet Web Portal. http://bikenet.cs.dartmouth.edu. ACM Portal - http://portal.acm.org References

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