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Reliability Investigation of LED Devices for Public Light Applications

Durability, Robustness and Reliability of Photonic Devices Set

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Aerospace Actuators 2

Signal-by-Wire and Power-by-Wire

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Flash Memory Integration

Performance and Energy Considerations

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Mechanics of Aeronautical Solids, Materials and Structures

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Engineering Investment Process

Making Value Creation Repeatable

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Space Strategy

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Distributed Systems

Concurrency and Consistency

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Fatigue of Textile and Short Fiber Reinforced Composites

Durability and Ageing of Organic Composite Materials Set Volume 1

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Management of the Effects of Coastal Storms

Policy, Scientific and Historical Perspectives

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Computational Color Science

Variational Retinex-like Methods

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Bio-inspired Networking

Daniel Camara, Telecom ParisTech, France

ISBN: 9781785480218

Publication Date: August 2015   Hardback   144 pp.

75.00 USD

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Bio-inspired techniques are based on principles, or models, of biological systems. In general, natural systems present remarkable capabilities of resilience and adaptability. The author of this book explores how bio-inspired methods can solve different problems linked to computer networks.
Future networks are expected to be autonomous, scalable and adaptive. During millions of years of evolution, nature has developed a number of different systems that present these and other characteristics required for the next generation networks. A series of bio-inspired methods have already been successfully used to solve the most diverse problems linked to computer networks. This book presents some of these techniques from a theoretical and practical point of view and provides a broad view of the field showing how different bio-inspired techniques are applied to computer networks.
It is aimed at network designers, system administrators, network researchers, graduate students and industrialists interested in the development of efficient solutions for distributed systems problems, as well as curious people interested in learning about biologically-inspired techniques


1. Evolution and Evolutionary Algorithms.
2. Chemical Computing.
3. Nervous System.
4. Swarm Intelligence (SI).

About the Authors

Daniel Camara is a Research Engineer at Telecom ParisTech, in France, currently working in the System on Chip Laboratory (LABSOC). His research interests include wireless networks, distributed systems, quality of software and artificial intelligence algorithms.


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