Social Sensing: Building Reliable Systems on Unreliable Data [Paperback]

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

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 RlcB