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Participatory Sensing. 4921013439 Huang, Ming-Chun. Outline. Motivation Alternatives Partisan Architecture Other Applications and Campaigns. A Case. Asthma rates v.s Truck traffic density in New York City. Year-Round Particle Pollution

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Participatory sensing

Participatory Sensing


Huang, Ming-Chun


  • Motivation

  • Alternatives

  • Partisan Architecture

  • Other Applications and Campaigns

A case
A Case

  • Asthma 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 solution
A Traditional Solution

  • Utilizing specialized equipments and manpower from community to document commercial truck traffic.


  • Link 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 thought
Take a second thought

  • Do 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 sensing
An Alternative: Participatory Sensing

  • Participatory 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 techniques
Suggested Techniques

  • Adaptive 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
Grassroots (bottom-up)


  • 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
Partisan Architecture

  • Places users in the loop of the sensing process and aims to maximize the credibility of data they collect.

  • In situ measurement

    Core network service

In situ measurement
In situ measurement

  • CENS : Center for Embedded Network Sensing

  • headquartered at UCLA

  • USC 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 service
Core Network Service

  • What we are concerned about?

    ans: Network-level mechanisms

    Quality Checks & Privacy Control

    Context Verification & Resolution Control

    key: Mediator(Access Point & Router level)

Mediator s job
Mediator’s Job

  • Location & Time

  • Phenomena of interest

  • Privacy

Network attested context location t ime
Network-Attested Context(location &time)

  • Credibility for decision-making


  • Tagging data packets

    RF Signal StrenghLocalizaiton & Timestamp

Physical context phenomena of interest
Physical context(phenomena of interest)

  • Directional microphone deployment.

    Ex: Orientation

    Ex: Team Localization

  • Averaging with reputation information.

Context resolution control privacy
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 campaign
Application and Campaign

  • Public health: Chronic and Environmental

  • Urban Planning: City or Park development

  • Cultural identity and creative expression

    Ubiquity of image capture with presence-based authentication.

  • Natural resource management

Human model

  • Initiator : Creator and Problem definer

  • Gatherer: Mobile User

  • Evaluator: Verify and Classify collected data.

  • Analyst : Process, Interpret, Present data and Give conclusions

    Future Goal: Distributed Data-Gathering


Let participatory sensing become

“Citizen Sensing” to uncover

something was previous unobservable.


  • Participatory Sensing

  • Particle Pollution Description

  • Team Localization: A Maximum Likelihood


Thanks for your attention

Thanks For Your Attention

Any Question???