Mobile Crowdsensing Current State and Future Challenges

CS 495Application Development for Smart Devices

Mobile CrowdsensingCurrent 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…

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.

User at Front End

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.

User at Front End

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.

• ……

Participatory Sensing Opportunistic Sensing

Users actively engage in the data collection activity.

Users manually determine how, when, what, where to

sample.

Higher burdens or costs.

Can avoid phone context issues.

Takes random sample which is application defined.

Easy to gather large amount data in small time.

Can’t avoid phone context issues.

Lower burdens or costs if contextual problems are

handled.

Filtering Data by Handling Privacy Issues & Localization.

Dataset is ready for research !!!

The Paradigms…

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