Description
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
Table of Contents
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
