Social Sensing

Building Reliable Systems on Unreliable Data

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Internet
Cover of the book Social Sensing by Dong Wang, Tarek Abdelzaher, Lance Kaplan, Elsevier Science
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Author: Dong Wang, Tarek Abdelzaher, Lance Kaplan ISBN: 9780128011317
Publisher: Elsevier Science Publication: April 17, 2015
Imprint: Morgan Kaufmann Language: English
Author: Dong Wang, Tarek Abdelzaher, Lance Kaplan
ISBN: 9780128011317
Publisher: Elsevier Science
Publication: April 17, 2015
Imprint: Morgan Kaufmann
Language: English

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.

  • Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability
  • Presents novel theoretical foundations for assured social sensing and modeling humans as sensors
  • Includes case studies and application examples based on real data sets
  • Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book
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Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.

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