Transcript 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
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Industrial, structural, environmental
monitoring systems, military systems,
etc.
Characteristics of Existing Systems
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Small-scale, short-lived, mostly-static
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Application-specific
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Multi-hop wireless
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Very energy-constrained
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Mobility not an issues of driving factor
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People out of the loop
Sensor networks at cross roads?
They don’t impact our everyday
lives, why?
Follow the people. Follow the money.
New Frontier for Sensing: Urban
Timescape
QuickTime™ and a
YUV420 codec decompressor
are needed to see this picture.
Ron Fricke, timescape is a day in the life of a city (edited version)
People-centric, mobility counts,
scale matters.
Urban Sensing Apps.
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Noise mapping
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http://www.noisemapping.org/
London Noise Map
Emotion mapping
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http://biomapping.net
Congestion charging
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http://www.cclondon.com/
Downing Street Noise Map
Emotion Maps of London
Congestion Map London
People-Centric Sensing Apps.
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Heath care applications
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Emergency care (Codeblue), assited
living (AlarmNet)
Recreational applications
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Running (Nikeplus), dancing
(interactive dance ensembles)
Urban gaming
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http://www.comeoutandplay.org/index.
php
Emerging Urban Sensing Classes
“Personal Sensing” for individuals
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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
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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
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e.g., noisemapping, others
Providers (e.g., towns, organizations, enterprises) will have
to invest in build out - costly
Need to exploit existing computing,
sensing, wireless breakthroughs and
infrastructure to support these emerging
urban sensing classes
What is MetroSense?
Architecture for large-scale sensing
based on mobile sensors.
Captures interaction between
people, and, between people and
their surroundings.
Enables general purpose
programming of the infrastructure.
Based on three design principles
that promote low cost, scalability,
and performance.
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.
Gains scalability and sensing
coverage via people-centric
mobility, and its adaptive “sphere of
interaction” design.
Goal is to study and evaluate an
“opportunistic sensor network”
paradigm
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
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
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
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
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
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
Characteristics of MetroSense
•
Large-scale, long-lived, mostly-mobile
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Application-agnostic
•
Very limited multi-hop wireless
•
Not energy-constrained
•
Mobility is a driving factor
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People in the loop
Characteristics of MetroSense
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Large-scale, long-lived, mostly-mobile
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Application-agnostic
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Very limited multi-hop wireless
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Not energy-constrained
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Mobility is a driving factor
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People in the loop
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Security, trust, and privacy important
My sense of “urban” has changed
… from here
.. to here
Sensing across a Large Area is
Challenging
Sensing across a Large Area is
Challenging
Imagine the
Green Is
Time Square ;-)
Sensing across a Large Area is
Challenging
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Some simple questions
one might ask
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How many people are
sitting, running, walking on
the Green?
Where is Andrew on the
Green?
Noise, temperature,
allergies distribution across
the Green [now, 10AM10PM, etc.]
Others
Ubisense
Cost ($)
Tiered
Mesh
MetroSense
Fidelity (Samples/Area)
Sensing across a Large Area is
Challenging - what scales?
Ubisense
Tiered
Mesh
MetroSense
Building Campus
Town
City
Scale
Building Campus
Town
City
Scale
What is MetroSense?
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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
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Sensor Access Points (SAPs)
Mobile Sensors (MSs)
Static Sensors (SSs)
Operations
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Opportunistic Tasking, Sensing,
Collection
Opportunistic Delegation Model
(ODM)
Sensing across a Large Area is Challenging a proposed Campus-wide Sensor Network
SAP locations - Aruba APs
Sensing Coverage using MetroSense
MetroSense Infrastructure
Sensor Access Point (SAP)
Sensor devices
People-centric sensing apps
Dartmouth Pulse
BikeNet
Interacts with static
sensor clouds
MetroSense Operations
opportunistic
tasking
comms &
ground-truth
sensing
opportunistic opportunistic
sensing
collection
limited
peering
Opportunistic Delegation Model (ODM)
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TX range
direct
delegation
sensing
range
indirect
delegation
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
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Sensing, tasking, collection and “data
muling”
Enables new services (ODM Primitives)
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Virtual sensing range, virtual collection
range, virtual static sensor, virtual mobile
network
Design challenges
Data mule
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sensing “space” of interest
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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
Virtual Sensing Range - an ODM
Primitive/Service
Virtual Sensing Range
TX range
sensing
range
Area of Interest “sensing space”
Virtual Sensing Range - Experimental
Result
Virtual Sensing Range
New Transports for Opportunistic
Tasking and Collection
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Reliable, secure needs
Limited “rendezvous” time,
mobility, probabilistic
sensing/collection, delay
tolerant collection
New transport needs
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Lazy uploading
Lazy tasking
Direction-based muling
Adaptive multihop
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
Skiscape - @ Dartmouth Skiway
Existing Urban Sensing Initiatives
Nokia’s SensorPlanet
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CENS Urban Sensing Summit (May 2006)
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http://www.sensorplanet.org/
http://bigriver.remap.ucla.edu/remap/index.php/Ur
ban_Sensing_Summit
CitySense (BBN/Harvard)
MetroSense (Dartmouth/ Columbia)
Others?
Conclusion
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Next wave in sensor networks is “people in
the loop and not out of loop” sensor
networks
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Scale and mobility matters and are
challenging in terms of architectural design
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Didn’t talk about security, privacy, and trust
that are central to this effort
What is MetroSense?
Ultimately, its about a new
wireless sensor edge for Internet
Thanks for listening!