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Participatory Sensing 4921013439 Huang, Ming-Chun.
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Transcript of Participatory Sensing 4921013439 Huang, Ming-Chun.
A CaseAsthma rates v.s Truck traffic
density in New York City.
Year-Round Particle Pollution◦What it is: Particle pollution refers to
a mix of very tiny solid and liquid particles that are in the air we breathe.
◦Consequence: Asthma attacks, Lung cancer, and Cardiovascular disease
A Traditional SolutionUtilizing specialized equipments
and manpower from community to document commercial truck traffic.
ResultLink truck traffic density with
diesel exhaust particle pollution
Uncover illegal truck routes.
Data can influence public policy and health.
But… It seems… problematic…
Take a second thoughtDo the data accurate enough?Are the people in the community
objective enough?
Even if hopefully everyone is careful,
honest and neutral…
But… this brutal force method takes too much money and too much time.
An Alternative: Participatory SensingParticipatory Sensing: Let everyone be a debugger and
integrate most of small piece of information.
Enhance and Systematize those existing methodologies.
Increase the quantity, quality and credibility of data with less cost and more convenience.
Suggested TechniquesAdaptive data collection protocols.Geotagging with network-attested
location and time -> Credibility Ask user to repeat and correct his
observation before environment changes
Upload from where there is not yet network-connected
Save users’ time to concentrate on where there is insufficient coverage in dataset
Gather human activity patterns.
Grassroots (bottom-up)
Benefits:Low cost without waiting for a
formal project or funding.
Let every citizen can be responsive to their environmental anomalies and examine expert assessments and judgments.
Partisan Architecture Places users in the
loop of the sensing process and aims to maximize the credibility of data they collect.
In situ measurementCore network service
In situ measurementCENS : Center for Embedded Network
Sensingheadquartered at UCLAUSC also participate in CENS-led research
In situ measurements Remote sensing.
Require that the instrumentation be located directly at the point of interest and in contact with the subject of interest.
Core Network ServiceWhat we are concerned about?ans: Network-level mechanisms
Quality Checks & Privacy Control
Context Verification & Resolution Control
key: Mediator(Access Point & Router level)
Network-Attested Context(location & time)
Credibility for decision-making
By…Tagging data packets RF Signal Strengh Localizaiton &
Timestamp
Physical context(phenomena of interest)
Directional microphone deployment.Ex: OrientationEx: Team Localization
Averaging with reputation information.
Context Resolution Control (Privacy)
Follow user-defined/default privacy rule.
May need to deliberately hide the context info : Selective Sharing Concept
Add some random jitter to packets.Routed through multi-mediator to
hide network identifier: IP, host name.
Application and CampaignPublic health: Chronic and
Environmental Urban Planning: City or Park
developmentCultural identity and creative
expressionUbiquity of image capture with presence-based authentication.
Natural resource management
Human ModelInitiator : Creator and Problem definerGatherer: Mobile UserEvaluator: Verify and Classify collected
data.Analyst : Process, Interpret, Present
data and Give conclusions
Future Goal: Distributed Data-Gathering
Conclusion
Let participatory sensing become “Citizen Sensing” to uncoversomething was previous
unobservable.
ReferenceParticipatory Sensing Particle Pollution DescriptionTeam Localization: A Maximum Li
kelihoodApproach