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Mobile Crowdsensing Current State and Future Challenges

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 ….

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Mobile Crowdsensing Current State and Future Challenges

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  1. CS 495 Application Development for Smart Devices Mobile CrowdsensingCurrent State and Future Challenges • Mobile Crowdsensing. • Overview of Crowdsensing applications. • MCS: Unique Characteristics

  2. 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.

  3. 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.

  4. 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

  5. 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. • ……

  6. 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 !!!

  7. 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)

  8. The Research Challenges of MCS… Localized Analytics Resource Limitations Privacy Aggregate Analytics Architecture

  9. 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.

  10. 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)?

  11. Privacy • Approaches : • Anonymization ; which removes any identifying information • from the sensor data before sharing it with a third party. • Secure multiparty computation, where cryptographic • techniquesare used to transform the data to preserve • the privacy of an individual.

  12. 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.

  13. 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:AnApproach to Smarter Cities. by RóbertSzabó Dept. of Telecommunications and Media Informatics Budapest University of Technology and Economics

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