Social Sensing: Building Reliable Systems on Unreliable Data

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9780128008676: Social Sensing: Building Reliable Systems on Unreliable Data

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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From the Back Cover:

Increasingly, human beings are sensors engaging directly with the mobile internet. Individuals can now share real-time experiences at an unprecedented scale, with GPS devices and camera phones, mobile Internet connectivity, and social media. Social Sensing: Building Reliable Systems on Unreliable Data offers a look into 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? Unlike traditional sensor networks, where sensor reliability and fault models are clearly understood, a big challenge in social sensing stems from the fact that data sources are often unknown to the collector, making it hard to ascertain not only correctness of observations but also reliability of 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; data which can be noisy, unreliable, and possibly erroneous. The book offers theoretical foundations to support emerging data-driven cyber-physical applications, and touches on key issues such as privacy in a highly interconnected and instrumented world. The authors share the latest research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion to present solutions to this problem.

About the Author:

Dong Wang is an Assistant Professor at the Department of Computer Science and Engineering, the University of Notre Dame. He received his Ph.D. in Computer Science from University of Illinois at Urbana Champaign (UIUC) in 2012, an M.S. degree from Peking University in 2007 and a B.Eng. from the University of Electronic Science and Technology of China in 2004, respectively. Dong Wang has published over 30 technical papers in conferences and journals, including IPSN, ICDCS, IEEE JSAC, IEEE J-STSP, and ACM ToSN. His research on social sensing resulted in software tools that found applications in academia, industry, and government research labs. His work was widely reported in talks, keynotes, panels, and tutorials, including at IBM Research, ARL, CPSWeek, RTSS, IPSN, and the University of Michigan, to name a few. Wang’s interests lie in developing analytic foundations for reliable information distillation systems, as well as the foundations of data credibility analysis, in the face of noise and conflicting observations, where evidence is collected by both humans and machines.

Tarek Abdelzaher is currently a Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 200 refereed publications in cyber-physical systems, distributed computing, sensor networks, and control, with emphasis on human-in-the-loop challenges. He is an Editor-in-Chief of the Journal of Real-Time Systems, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal. Abdelzaher's research interests lie broadly in understanding and controlling performance properties of computing systems that interact with both a physical environment and social context in the face of increasing complexity, distribution, and degree of embedding in the physical world.

Lance M. Kaplan received the B.S. degree with distinction from Duke University, Durham, NC, in 1989 and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, in 1991 and 1994, respectively, all in Electrical Engineering. From 1987-1990, Dr. Kaplan worked as a Technical Assistant at the Georgia Tech Research Institute. He held a National Science Foundation Graduate Fellowship and a USC Dean's Merit Fellowship from 1990-1993, and worked as a Research Assistant in the Signal and Image Processing Institute at the University of Southern California from 1993-1994. Then, he worked on staff in the Reconnaissance Systems Department of the Hughes Aircraft Company from 1994-1996. From 1996-2004, he was a member of the faculty in the Department of Engineering and a senior investigator in the Center of Theoretical Studies of Physical Systems (CTSPS) at Clark Atlanta University (CAU), Atlanta, GA. Currently, he is a researcher in the Networked Sensing and Fusion branch of the U.S. Army Research Laboratory. Dr. Kaplan serves as Editor-In-Chief for the IEEE Transactions on Aerospace and Electronic Systems (AES). In addition, he also serves on the Board of Governors of the IEEE AES Society and on the Board of Directors of the International Society of Information Fusion. He is a three time recipient of the Clark Atlanta University Electrical Engineering Instructional Excellence Award from 1999-2001. His current research interests include signal and image processing, automatic target recognition, information/data fusion, and resource management.

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Dong Wang, Tarek Abdelzaher, Lance Kaplan
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Book Description Book Condition: New. Publisher/Verlag: Morgan Kaufmann | Building Reliable Systems on Unreliable Data | 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 reliabilityPresents novel theoretical foundations for assured social sensing and modeling humans as sensorsIncludes case studies and application examples based on real data setsSupplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book | 1. Introduction2. Social Sensing Trends and Applications3. Mathematical Foundations4. Basic Fact-Finding5. Maximum Likelihood Estimation6. Confidence Bounds7. Conflicting Observations and Non-Binary Claims8. Understanding the Social Network9. Understanding Physical Dependencies10. Recursive Fact-finding11. Privacy12. Further Readings13. Conclusions and Remaining Challenges | Format: Paperback | Language/Sprache: english | 479 gr | 232x185x10 mm | 232 pp. Bookseller Inventory # K9780128008676

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Book Condition: New. Language: English . Brand New Book. 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. Bookseller Inventory # AA59780128008676

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2015. Paperback. Book Condition: New. Language: English . Brand New Book. 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. Bookseller Inventory # AA59780128008676

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