Participatory Sensing 4921013439 Huang, Ming-Chun.

Participatory Sensing4921013439Huang, Ming-Chun

OutlineMotivationAlternativesPartisan ArchitectureOther Applications and

Campaigns

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)

Mediator’s JobLocation & Time

Phenomena of interest

Privacy

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

Thanks For Your Attention

Any Question???