Social Sensing

Building Reliable Systems on Unreliable Data

1st Edition - March 23, 2015
Authors: Dong Wang, Tarek Abdelzaher, Lance Kaplan
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 0 0 8 6 7 - 6
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 0 1 1 3 1 - 7

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Build… Read more

Purchase options

Institutional subscription on ScienceDirect

Request a sales quote

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.

Dedication

Acknowledgments

Authors

Foreword

Preface

Chapter 1: A new information age

Abstract
1.1 Overview
1.2 Challenges
1.3 State of the Art
1.4 Organization

Chapter 2: Social sensing trends and applications

Abstract
2.1 Information Sharing: The Paradigm Shift
2.2 An Application Taxonomy
2.3 Early Research
2.4 The Present Time
2.5 A Note on Privacy

Chapter 3: Mathematical foundations of social sensing: An introductory tutorial

Abstract
3.1 A Multidisciplinary Background
3.2 Basics of Generic Networks
3.3 Basics of Bayesian Analysis
3.4 Basics of Maximum Likelihood Estimation
3.5 Basics of Expectation Maximization
3.6 Basics of Confidence Intervals
3.7 Putting It All Together

Chapter 4: Fact-finding in information networks

Abstract
4.1 Facts, Fact-Finders, and the Existence of Ground Truth
4.2 Overview of Fact-Finders in Information Networks
4.3 A Bayesian Interpretation of Basic Fact-Finding
4.4 The Iterative Algorithm
4.5 Examples and Results
4.6 Discussion
Appendix

Chapter 5: Social Sensing: A maximum likelihood estimation approach

Abstract
5.1 The Social Sensing Problem
5.2 Expectation Maximization
5.3 The EM Fact-Finding Algorithm
5.4 Examples and Results
5.5 Discussion

Chapter 6: Confidence bounds in social sensing

Abstract
6.1 The Reliability Assurance Problem
6.2 Actual Cramer-Rao Lower Bound
6.3 Asymptotic Cramer-Rao Lower Bound
6.4 Confidence Interval Derivation
6.5 Examples and Results
6.6 Discussion
Appendix

Chapter 7: Resolving conflicting observations and non-binary claims

Abstract
7.1 Handling Conflicting Binary Observations
7.2 Handling Non-Binary Claims
7.3 Performance Evaluation
7.4 Discussion
Appendix

Chapter 8: Understanding the social network

Abstract
8.1 Information Propagation Cascades
8.2 A Binary Model of Human Sensing
8.3 Inferring the Social Network
8.4 A Social-Aware Algorithm
8.5 Evaluation
8.6 Discussion and Limitations

Chapter 9: Understanding physical dependencies: Social meets cyber-physical

Abstract
9.1 Correlations in the Physical World
9.2 Accounting for the Opportunity to Observe
9.3 Accounting for Physical Dependencies
9.4 Real-World Case Studies
9.5 Discussion
Appendix

Chapter 10: Recursive fact-finding

Abstract
10.1 Real Time Social Sensing
10.2 A Streaming Truth Estimation Model
10.3 Dynamics and the Recursive Algorithm
10.4 Performance Evaluation
10.5 Discussion

Chapter 11: Further readings

Abstract
11.1 Estimation Theory
11.2 Data Quality and Trust Analysis
11.3 Outlier Analysis and Attack Detection
11.4 Recommender Systems
11.5 Surveys and Opinion Polling

Chapter 12: Conclusions and future challenges

Abstract
12.1 Summary and Conclusions
12.2 Remaining Challenges and Future Work

References

Index

Dong Wang

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.

Affiliations and expertise

Assistant Professor, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

Tarek Abdelzaher

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.

Affiliations and expertise

Professor and Willett Faculty Scholar, Department of Computer Science, University of Illinois, Urbana Champaign, Champaign, IL, USA

Lance Kaplan

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.

Affiliations and expertise

Networked Sensing and Fusion Branch, U.S. Army Research Laboratory, Adelphia, MD, USA

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Social Sensing

Building Reliable Systems on Unreliable Data

Purchase options

Institutional subscription on ScienceDirect

Dong Wang

Tarek Abdelzaher

Lance Kaplan