This book presents a survey of the state-of-the art in the exciting and
timely topic of compressed sensing for distributed systems. It has to be
noted that, while compressed sensing has been studied for some time now,
its distributed applications are relatively new. Remarkably, such
applications are ideally suited to exploit all the benefits that
compressed sensing can provide. The objective of this book is to provide
the reader with a comprehensive survey of this topic, from the basic
concepts to different classes of centralized and distributed
reconstruction algorithms, as well as a comparison of these techniques.
This book collects different contributions on these aspects. It presents
the underlying theory in a complete and unified way for the first time,
presenting various signal models and their use cases. It contains a
theoretical part collecting latest results in rate-distortion analysis
of distributed compressed sensing, as well as practical implementations
of algorithms obtaining performance close to the theoretical bounds. It
presents and discusses various distributed reconstruction algorithms,
summarizing the theoretical reconstruction guarantees and providing a
comparative analysis of their performance and complexity. In summary,
this book will allow the reader to get started in the field of
distributed compressed sensing from theory to practice. We believe that
this book can find a broad audience among researchers, scientists, or
engineers with very diverse backgrounds, having interests in
mathematical optimization, network systems, graph theoretical methods,
linear systems, stochastic systems, and randomized algorithms. To help
the reader become familiar with the theory and algorithms presented,
accompanying software is made available on the authors' web site,
implementing several of the algorithms described in the book. The only
background required of the reader is a good knowledge of advanced
calculus and linear algebra.
