Computing in Communication Networks
From Theory to Practice
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Product details:
- Publisher Elsevier Science
- Date of Publication 21 May 2020
- ISBN 9780128204887
- Binding Paperback
- No. of pages522 pages
- Size 234x190 mm
- Weight 1060 g
- Language English 64
Categories
Long description:
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.
MoreTable 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:ï¿1⁄2A 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
Introduction to Data Compression
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