1. MetroSense People-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?
(Mark Weiser’s vision - 20 years on).

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 Emotion mapping Congestion charging
Emotion mapping
Congestion charging
London Noise Map
The Bio Mapping tool allows the wearer to record their Galvanic Skin Response (GSR), which is a simple indicator of emotional arousal in conjunction with their geographical location. This can be used to plot a map that highlights point of high and low arousal. By sharing this data we can construct maps that visualise where we as a community feel stressed and excited
Noise - unwanted sound - is a universal problem and most of us have been affected by it at some point in our lives.The Noise Mapping England project is the first stage in the development of a National Ambient Noise Strategy for England. The project aims to establish the environmental noise climate across England.Defra (Department for Environment, Food and Rural Affairs) has now launched the London Road Traffic Noise Map, which is fundamental to the Noise Mapping England project.To view the maps, click on the London area
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

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.
Architecture for large-scale sensing based on mobile sensors (most people, but also vehicle mobility - cars, buses, bikes)
+ also interaction with static sensor webs

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

36. Sensing Coverage using MetroSense

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

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

39. Opportunistic Delegation Model (ODM)
TX range
sensing range
direct delegation
indirect delegation
Data mule
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
TX range
sensing range
Area of Interest - “sensing space”

41. Virtual Sensing Range - Experimental Result

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
CENS Urban Sensing Summit (May 2006)
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!