Cooperative and Graph Signal Processing

Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience.With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings.
- Presents the first book on cooperative signal processing and graph signal processing- Provides a range of applications and application areas that are thoroughly covered- Includes an editor in chief and associate editor from the IEEE Transactions on Signal Processing and Information Processing over Networks who have recruited top contributors for the book

PART 1 BASICS OF INFERENCE OVER NETWORKS1. Asynchronous Adaptive Networks2. Estimation and Detection Over Adaptive Networks3. Multitask Learning Over Adaptive Networks With Grouping Strategies4. Bayesian Approach to Collaborative Inference in Networks of Agents5. Multiagent Distributed Optimization6. Distributed Kalman and Particle Filtering7. Game Theoretic LearningPART 2 SIGNAL PROCESSING ON GRAPHS8. Graph Signal Processing9. Sampling and Recovery of Graph Signals10. Bayesian Active Learning on Graphs11. Design of Graph Filters and Filterbanks12. Statistical Graph Signal Processing: Stationarity and Spectral Estimation13. Inference of Graph Topology14. Partially Absorbing Random Walks: A Unified Framework for Learning on GraphsPART 3 DISTRIBUTED COMMUNICATIONS, NETWORKING, AND SENSING15. Methods for Decentralized Signal Processing With Big Data16. The Edge Cloud: A Holistic View of Communication, Computation, and Caching17. Applications of Graph Connectivity to Network Security18. Team Methods for Device Cooperation in Wireless Networks19. Cooperative Data Exchange in Broadcast Networks20. Collaborative Spectrum Sensing in the Presence of Byzantine AttackPART 4 SOCIAL NETWORKS21. Dynamics of Information Diffusion and Social Sensing22. Active Sensing of Social Networks: Network Identification From Low-Rank Data23. Dynamic Social Networks: Search and Data Routing24. Information Diffusion and Rumor Spreading25. Multilayer Social Networks26. Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network FormationPART 5 APPLICATIONS27. Genomics and Systems Biology28. Diffusion Augmented Complex Extended Kalman Filtering for Adaptive Frequency Estimation in Distributed Power Networks29. Beacons and the City: Smart Internet of Things30. Big Data31. Graph Signal Processing on Neuronal Networks