CS 495
Application Development for Smart Devices
Mobile Crowdsensing
Current State and Future Challenges
• Mobile Crowdsensing.
• Overview of Crowdsensing applications.
• MCS: Unique Characteristics
Introduction to Mobile Crowdsensing…
Mobile Crowdsensing means the integration of
sensors that can be used for gathering
materialistic or non-materialistic information,
people who use these sensors & obviously
their global participation.
Introduction to Mobile Crowdsensing…
User at Front End
Mobile Crowdsensing means the integration of
sensors that can be used for gathering
materialistic or non-materialistic information,
people who use these sensors & obviously
their global participation.
Introduction to Mobile Crowdsensing…
User at Front End
Mobile Crowdsensing means the integration of
sensors that can be used for gathering
materialistic or non-materialistic information,
people who use these sensors & obviously
their global participation.
Web Service at Back End
Community Phenomena & Monitorization…
Monitoring common phenomenon…
•
Pollution (air/noise) levels in a neighborhood.
•
Real-time traffic patterns.
•
Pot holes on roads.
•
Road closures and transit timings.
•
……
The Paradigms…
Participatory Sensing
Opportunistic Sensing
Users actively engage in the
data collection activity.
Takes random sample which is
application defined.
Users manually determine
how, when, what, where to
sample.
Easy to gather large amount
data in small time.
Can avoid phone context
issues.
Can’t avoid phone context
issues.
Higher burdens or costs.
Lower burdens or costs if
contextual problems are
handled.
Filtering Data by Handling Privacy Issues & Localization.
Dataset is ready for research !!!
The Concept of “Internet of Things”…
“When objects can both sense the environment and communicate,
they become tools for understanding complexity and responding to
it swiftly. What’s revolutionary in all this is that these physical
information systems are now beginning to be deployed, and some
of them even work largely without human intervention.”
--- (McKinsey & Company, 2010)
The Research Challenges of MCS…
Localized Analytics
Resource Limitations
Privacy
Aggregate Analytics
Architecture
Localized Analytics
Raw sensing data is collected on devices and local analytics process it to
produce consumable data for applications. After privacy preservation, the
data is sent to the backend and aggregate analytics will further process it
for different applications.
Resource Limitations
• How do multiple applications on the same device utilize
energy, bandwidth, and computation resources
without significantly affecting the data quality of
each other?
• How does scheduling of sensing tasks
occur across multiple devices with diverse sensing
capabilities and availabilities (which can change
dynamically)?
Privacy
Approaches :
• Anonymization ; which removes any identifying information
from the sensor data before sharing it with a third party.
• Secure multiparty computation, where cryptographic
techniques are used to transform the data to preserve
the privacy of an individual.
MCS : Unique Characteristics…
This is a double sided sword…….
The intelligence and mobility of
humans can be leveraged to help
applications collect higher quality or
semantically complex data that may
otherwise require sophisticated
hardware and software.
On the other hand, humans naturally have
privacy concerns and personal preferences
that are not necessarily in the best
interests
of
MCS
applications
but
applications have to live within these
constraints.
References
1 . Mobile Crowdsensing: Current State and Future Challenges.
by Raghu K. Ganti, Fan Ye, and Hui Lei
IBM T. J. Watson Research Center, Hawthorne, NY
2. Mobile Crowd Sensing:An Approach to Smarter Cities.
by Róbert Szabó
Dept. of Telecommunications and Media Informatics
Budapest University of Technology and Economics