Bio-inspired techniques are based on principles, or models, of biological systems. In general, natural systems present remarkable capabilities of resilience and adaptability. In this book, we explore 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. Indeed, a series of bio-inspired methods have 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.
- Discusses the key concepts of bio-inspired networking to aid you in finding efficient networking solutions
- Delivers examples of techniques both in theoretical concepts and practical applications
- Helps you apply nature's dynamic resource and task management to your computer networks
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Learn bio-inspired networking techniques from a theoretical and practical point of view
| Introduction |
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Chapter 1 Evolution and Evolutionary Algorithms |
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1 | (30) |
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1.1 Brief introduction to evolution |
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2 | (4) |
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1.2 Mechanisms of evolution |
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6 | (3) |
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6 | (1) |
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1.2.3 Sexual reproduction and recombination |
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7 | (1) |
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8 | (1) |
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9 | (1) |
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9 | (4) |
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10 | (2) |
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12 | (1) |
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1.4 Applications on networks |
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1.4.1 Network positioning |
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13 | (6) |
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19 | (6) |
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25 | (1) |
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25 | (2) |
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27 | (4) |
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Chapter 2 Chemical Computing |
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31 | (14) |
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34 | (2) |
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2.2 Applications on networks |
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36 | (5) |
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36 | (2) |
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38 | (3) |
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41 | (1) |
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42 | (3) |
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45 | (36) |
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3.1 Nervous system hierarchy |
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46 | (3) |
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3.1.1 Central nervous system |
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47 | (1) |
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3.1.2 Peripheral nervous system |
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47 | (2) |
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49 | (2) |
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51 | (3) |
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54 | (2) |
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3.5 Artificial neural networks |
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56 | (10) |
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57 | (2) |
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3.5.2 Interconnecting perceptrons |
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59 | (3) |
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62 | (1) |
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3.5.4 The backpropagation algorithm |
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63 | (3) |
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3.6 Applications on networks |
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66 | (8) |
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3.6.1 ANN in intrusion detection systems |
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67 | (2) |
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69 | (2) |
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71 | (3) |
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74 | (1) |
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75 | (6) |
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Chapter 4 Swarm Intelligence (SI) |
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81 | (22) |
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4.1 Ant colony optimization |
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86 | (1) |
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4.2 Applications on networks |
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87 | (6) |
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4.2.1 Ants colony on routing |
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87 | (3) |
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4.2.2 Ants colony on intrusion detection |
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90 | (3) |
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4.3 Particle swarm optimization |
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93 | (2) |
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4.4 Applications on networks |
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95 | (3) |
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4.4.1 Particle swarm on node positioning |
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95 | (1) |
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4.4.2 Particle swarm on intrusion detection |
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96 | (2) |
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98 | (1) |
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99 | (4) |
| Glossary |
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103 | (4) |
| Index |
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107 | |
Daniel Câmara is a Research Engineer at Telecom ParisTech, in France, currently working in the System on Chip Laboratory (LABSOC). His research interestsinclude wireless networks, distributed systems, quality of software and artificial intelligence algorithms.
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