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Computing in Communication Networks
From Theory to Practice
Presents the theory and applications behind the latest computing technologies as applied to communication networks
Frank H.P. Fitzek (Edited by), Fabrizio Granelli (Edited by), Patrick Seeling (Edited by)
9780128204887, Elsevier Science
Paperback, published 21 May 2020
522 pages, Approx. 100 illustrations
23.4 x 19 x 3.2 cm, 1.07 kg
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.
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
Subject Areas: Network management [UTF], WAP [wireless technology TJKW], Communications engineering / telecommunications [TJK]
