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.
