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MetroSense People-Centric Urban Sensing

MetroSense People-Centric Urban Sensing. Andrew T. Campbell, Shane B. Einseman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Perterson Dartmouth College/ Columbia University. Focus of Sensor Network Research. Industrial, structural, environmental monitoring systems, military systems, etc.

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MetroSense People-Centric Urban Sensing

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  1. MetroSensePeople-Centric Urban Sensing Andrew T. Campbell, Shane B. Einseman, Nicholas D. Lane, Emiliano Miluzzo, Ronald Perterson Dartmouth College/ Columbia University

  2. Focus of Sensor Network Research • Industrial, structural, environmental monitoring systems, military systems, etc.

  3. Characteristics of Existing Systems • Small-scale, short-lived, mostly-static • Application-specific • Multi-hop wireless • Very energy-constrained • Mobility not an issues of driving factor • People out of the loop

  4. Sensor networks at cross roads?

  5. They don’t impact our everyday lives, why?

  6. Follow the people. Follow the money.

  7. New Frontier for Sensing: Urban Timescape Ron Fricke, timescape is a day in the life of a city (edited version)

  8. People-centric, mobility counts, scale matters.

  9. Urban Sensing Apps. • Noise mapping • http://www.noisemapping.org/ • Emotion mapping • http://biomapping.net • Congestion charging • http://www.cclondon.com/ London Noise Map Downing Street Noise Map Emotion Maps of London Congestion Map London

  10. People-Centric Sensing Apps. • Heath care applications • Emergency care (Codeblue), assited living (AlarmNet) • Recreational applications • Running (Nikeplus), dancing (interactive dance ensembles) • Urban gaming • http://www.comeoutandplay.org/index.php

  11. Emerging Urban Sensing Classes • “Personal Sensing” for individuals • e.g., nikeplus, sensor-enabled cellphone apps., health care • Great potent for commercial success (catch the ipod generation) - youthful early adopters • “Peer Sensing” for groups • e.g., urban gaming, peers apps. • Could be an explosive growth because of existing gaming users • “Utility Sensing” (system-wide) provides utility to a large population of potential users • e.g., noisemapping, others • Providers (e.g., towns, organizations, enterprises) will have to invest in build out - costly

  12. Need to exploit existing computing, sensing, wireless breakthroughs and infrastructure to support these emerging urban sensing classes

  13. What is MetroSense?

  14. Architecture for large-scale sensing based on mobile sensors.

  15. Captures interaction between people, and, between people and their surroundings.

  16. Enables general purpose programming of the infrastructure.

  17. Based on three design principles that promote low cost, scalability, and performance.

  18. Importantly, mobile people-centric sensors run their own apps. (i.e., personal sensing, peer sensing), and, in parallel support “symbiotic sensing” (i.e., utility sensing, peer sensing) of other users in a transparent manner.

  19. Gains scalability and sensing coverage via people-centric mobility, and its adaptive “sphere of interaction” design.

  20. Goal is to study and evaluate an “opportunistic sensor network” paradigm

  21. Characteristics of Existing Systems • Small-scale, short-lived, mostly-static • Application-specific • Multi-hop wireless • Very energy-constrained • Mobility not an issues of driving factor • People out of the loop

  22. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-specific • Multi-hop wireless • Very energy-constrained • Mobility not an issues of driving factor • People out of the loop

  23. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-agnostic • Multi-hop wireless • Very energy-constrained • Mobility not an issues of driving factor • People out of the loop

  24. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-agnostic • Very limited multi-hop wireless • Very energy-constrained • Mobility not an issues of driving factor • People out of the loop

  25. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-agnostic • Very limited multi-hop wireless • Not energy-constrained • Mobility not an issues of driving factor • People out of the loop

  26. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-agnostic • Very limited multi-hop wireless • Not energy-constrained • Mobility is a driving factor • People out of the loop

  27. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-agnostic • Very limited multi-hop wireless • Not energy-constrained • Mobility is a driving factor • People in the loop

  28. Characteristics of MetroSense • Large-scale, long-lived, mostly-mobile • Application-agnostic • Very limited multi-hop wireless • Not energy-constrained • Mobility is a driving factor • People in the loop • Security, trust, and privacy important

  29. My sense of “urban” has changed … from here .. to here

  30. Sensing across a Large Area is Challenging

  31. Sensing across a Large Area is Challenging Imagine the Green Is Time Square ;-)

  32. Sensing across a Large Area is Challenging • Some simple questions one might ask • How many people are sitting, running, walking on the Green? • Where is Andrew on the Green? • Noise, temperature, allergies distribution across the Green [now, 10AM-10PM, etc.] • Others

  33. Sensing across a Large Area is Challenging - what scales? Fidelity (Samples/Area) Ubisense Cost ($) Ubisense Tiered Tiered Mesh Mesh MetroSense MetroSense Building Campus Town City Scale Building Campus Town City Scale

  34. What is MetroSense? • Relies on the random or not so random mobility of people • Task sensors to “collect” sensor data and deliver it opportunistically • Offers in delay-tolerant sensing • Infrastructure • Sensor Access Points (SAPs) • Mobile Sensors (MSs) • Static Sensors (SSs) • Operations • Opportunistic Tasking, Sensing, Collection • Opportunistic Delegation Model (ODM)

  35. SAP locations - Aruba APs Sensing across a Large Area is Challenging - a proposed Campus-wide Sensor Network

  36. Sensing Coverage using MetroSense

  37. MetroSense Infrastructure People-centric sensing apps Sensor Access Point (SAP) Sensor devices BikeNet Dartmouth Pulse Interacts with static sensor clouds

  38. MetroSense Operations comms & ground-truth sensing opportunistic tasking opportunistic sensing opportunistic collection limited peering

  39. TX range direct delegation sensing range indirect delegation Data mule Opportunistic Delegation Model (ODM) • Goal is to extend sensing coverage • Application requires sensed modality ß from “space” during [t1, t2] • Delegate “limited” responsibility for “limited” time • Direct and indirect delegation of roles • Sensing, tasking, collection and “data muling” • Enables new services (ODM Primitives) • Virtual sensing range, virtual collection range, virtual static sensor, virtual mobile network • Design challenges • Sensing range is dependent on modality • Limited comms. “rendezvous” time • Candidate sensor selection is challenging • Likelihood of a mobile reaching a targeted sensing space is probabilistic in nature • Delay tolerant characteristics of sensing and collection processes sensing “space” of interest

  40. Virtual Sensing Range - an ODM Primitive/Service Virtual Sensing Range TX range sensing range Area of Interest - “sensing space”

  41. Virtual Sensing Range - Experimental Result Virtual Sensing Range

  42. New Transports for Opportunistic Tasking and Collection • Reliable, secure needs • Limited “rendezvous” time, mobility, probabilistic sensing/collection, delay tolerant collection • New transport needs • Lazy uploading • Lazy tasking • Direction-based muling • Adaptive multihop

  43. Bikenet Road Warrior GPS Unit Measuring terrain slope Mote broadcasting sync msgs from GPS unit Measuring how fast i kick the ass of inconsiderate motorists Measuring pedal speed

  44. Skiscape - @ Dartmouth Skiway

  45. Existing Urban Sensing Initiatives • Nokia’s SensorPlanet • http://www.sensorplanet.org/ • CENS Urban Sensing Summit (May 2006) • http://bigriver.remap.ucla.edu/remap/index.php/Urban_Sensing_Summit • CitySense (BBN/Harvard) • MetroSense (Dartmouth/ Columbia) • Others?

  46. Conclusion • Next wave in sensor networks is “people in the loop and not out of loop” sensor networks • Scale and mobility matters and are challenging in terms of architectural design • Didn’t talk about security, privacy, and trust that are central to this effort

  47. What is MetroSense?

  48. Ultimately, its about a new wireless sensor edge for Internet

  49. Thanks for listening!

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