Computing in Communication Networks: From Theory to Practice

by Frank H. P. Fitzek PhD, Fabrizio Granelli, Patrick Seeling

Overview

Computing in Communication Networks: From Theory to Practice provides comprehensive details and practical implementation tactics on the novel concepts and enabling technologies at the core of the paradigm shift from store and forward (dumb) to compute and forward (intelligent) in future communication networks and systems. The book explains how to create virtualized large scale testbeds using well-established open source software, such as Mininet and Docker. It shows how and where to place disruptive techniques, such as machine learning, compressed sensing, or network coding in a newly built testbed. In addition, it presents a comprehensive overview of current standardization activities.

Specific chapters explore upcoming communication networks that support verticals in transportation, industry, construction, agriculture, health care and energy grids, underlying concepts, such as network slicing and mobile edge cloud, enabling technologies, such as SDN/NFV/ ICN, disruptive innovations, such as network coding, compressed sensing and machine learning, how to build a virtualized network infrastructure testbed on one’s own computer, and more.

Product Details

ISBN-13:	9780128204887
Publisher:	Elsevier Science
Publication date:	05/21/2020
Pages:	522
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Frank H. P. Fitzek is a Professor and head of the “Deutsche Telekom Chair of Communication Networks” at TU Dresden coordinating the 5G Lab Germany. He is the spokesman of the DFG Cluster of Excellence “Center for Tactile Internet” (CeTI).

He received his diploma (Dipl.-Ing.) degree in electrical engineering from the University of Technology – Rheinisch-Westfälische Technische Hochschule (RWTH) – Aachen, Germany, in 1997 and his Ph.D. (Dr.-Ing.) in Electrical Engineering from the Technical University Berlin, Germany in 2002 and became Adjunct Professor at the University of Ferrara, Italy in the same year. In 2003 he joined Aalborg University as Professor
.

Fabrizio Granelli is Associate Professor at the Dept. of Information Engineering and Computer Science (DISI) – University of Trento (Italy), IEEE ComSoc Director for Educational Services (2018-19) and Chair of Joint IEEE VTS/ComSoc Italian Chapter. He is Research Associate Professor at the University of New Mexico, NM, USA.
He received the M.Sc. and Ph.D. degrees from University of Genoa, Italy, in 1997 and 2001. He was visiting professor at the State University of Campinas (Brasil) and at the University of Tokyo (Japan), and IEEE ComSoc Distinguished Lecturer in 2012-15.
He is Associate Editor in Chief of IEEE Communications Surveys and Tutorials.

Patrick Seeling is a Professor in the Department of Computer Science at Central Michigan University, USA. He received his diploma (Dipl.-Ing.) degree in Industrial Engineering and Management from the Technical University Berlin, Germany, in 2002 and his his Ph.D. in Electrical Engineering from Arizona State University (ASU), USA, in 2005. He was an Associated Faculty at ASU until 2008 and an Assistant Professor at the University of Wisconsin-Stevens Point until 2011. In 2011, he joined Central Michigan University as Assistant Professor, where he became a tenured Associate Professor in 2015 and a Full Professor in 2018.

Table of Contents

PART 1 FUTURE COMMUNICATION NETWORKS AND SYSTEMS 1. On the need of computing in future communication networks 2. Standardization activities for future communication networks

PART 2 CONCEPTS 3. Network slicing 4. Mobile edge cloud 5. Content distribution

PART 3 ENABLING TECHNOLOGIES 6. Software-defined networks 7. Network function virtualization

PART 4 INNOVATION TRACK 8. Machine learning 9. Network coding 10. Compressed sensing

PART 5 BUILDING THE TESTBED 11. Mininet: An insant virtual network on your computer 12. Docker: Containerize your application 13. ComNetsEmu: A lightweight emulator

PART 6 EXAMPLES 14. Realizing network slicing 15. Realizing mobile edge clouds 16. Machine learning for routing 17. Machine learning for flow compression 18. Machine learning for congestion control 19. Machine learning for object detection 20. Network coding for transport 21. Network coding for storage 22. In-network compressed sensing 23. Security for mobile edge cloud

PART 7 EXTENSIONS 24. Connecting to the outer world 25. Integrating time-sensitive networking 26. Integrating software-defined radios

PART 8 TOOLS 27. Networking tools

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From the Publisher

Presents the theory and applications behind the latest computing technologies as applied to communication networks