Transcript Participatory Sensing - University of Southern California

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
◦ 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

Utilizing specialized equipments and
manpower from community to document
commercial truck traffic.
Result

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
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

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



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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)
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 measurement
Core network
service
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
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

Location & 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: Orientation
Ex: 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 Campaign
Public health: Chronic and Environmental
 Urban Planning: City or Park development
 Cultural identity and creative expression
Ubiquity of image capture with presencebased 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
Conclusion
Let participatory sensing become
“Citizen Sensing” to uncover
something was previous unobservable.
Reference
Participatory Sensing
 Particle Pollution Description
 Team Localization: A Maximum Likelihood
Approach

Thanks For Your Attention
Any Question???