+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Social Sensing

  • ID: 3084328
  • Book
  • 232 Pages
  • Elsevier Science and Technology
1 of 3

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

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Note: Product cover images may vary from those shown
2 of 3
1. Introduction
2. Social Sensing Trends and Applications
3. Mathematical Foundations
4. Basic Fact-Finding
5. Maximum Likelihood Estimation
6. Confidence Bounds
7. Conflicting Observations and Non-Binary Claims
8. Understanding the Social Network
9. Understanding Physical Dependencies
10. Recursive Fact-finding
11. Privacy
12. Further Readings
13. Conclusions and Remaining Challenges
Note: Product cover images may vary from those shown
3 of 3


4 of 3
Wang, Dong
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.
Abdelzaher, Tarek
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.
Kaplan, Lance
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.
Note: Product cover images may vary from those shown