Bio-Inspired Computing and Networking

Bio-Inspired Computing and Networking

by Yang Xiao
Released April 2016
Publisher(s): CRC Press
ISBN: 9781420080339

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

From ant-inspired allocation to a swarm algorithm derived from honeybees, this book explains how the study of biological systems can significantly improve computing, networking, and robotics. Containing contributions from leading researchers from around the world, the book investigates the fundamental aspects and applications of bio-inspired computing and networking. Presenting the latest advances in bio-inspired communication, computing, networking, clustering, optimization, and robotics, the book considers state-of-the art approaches, novel technologies, and experimental studies.

Table of contents

  1. Front cover
  2. Contents
  3. Preface
  4. Editor
  5. Acknowledgment
  6. Contributors
  7. Section I. ANIMAL BEHAVIORS AND ANIMAL COMMUNICATIONS
  8. Chapter 1. Animal Models for Computing and Communications: Past Approaches and Future Challenges (1/4)
  9. Chapter 1. Animal Models for Computing and Communications: Past Approaches and Future Challenges (2/4)
  10. Chapter 1. Animal Models for Computing and Communications: Past Approaches and Future Challenges (3/4)
  11. Chapter 1. Animal Models for Computing and Communications: Past Approaches and Future Challenges (4/4)
  12. Chapter 2. Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale (1/5)
  13. Chapter 2. Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale (2/5)
  14. Chapter 2. Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale (3/5)
  15. Chapter 2. Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale (4/5)
  16. Chapter 2. Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale (5/5)
  17. Section II. BIO-INSPIRED COMPUTING AND ROBOTS
  18. Chapter 3. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems (1/6)
  19. Chapter 3. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems (2/6)
  20. Chapter 3. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems (3/6)
  21. Chapter 3. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems (4/6)
  22. Chapter 3. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems (5/6)
  23. Chapter 3. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems (6/6)
  24. Chapter 4. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III (1/6)
  25. Chapter 4. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III (2/6)
  26. Chapter 4. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III (3/6)
  27. Chapter 4. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III (4/6)
  28. Chapter 4. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III (5/6)
  29. Chapter 4. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III (6/6)
  30. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (1/9)
  31. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (2/9)
  32. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (3/9)
  33. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (4/9)
  34. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (5/9)
  35. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (6/9)
  36. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (7/9)
  37. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (8/9)
  38. Chapter 5. BEECLUST: A Swarm Algorithm Derived from Honeybees (9/9)
  39. Chapter 6. Self-Organizing Dataand Signal Cellular Systems (1/6)
  40. Chapter 6. Self-Organizing Dataand Signal Cellular Systems (2/6)
  41. Chapter 6. Self-Organizing Dataand Signal Cellular Systems (3/6)
  42. Chapter 6. Self-Organizing Dataand Signal Cellular Systems (4/6)
  43. Chapter 6. Self-Organizing Dataand Signal Cellular Systems (5/6)
  44. Chapter 6. Self-Organizing Dataand Signal Cellular Systems (6/6)
  45. Chapter 7. Bio-Inspired Process Control (1/9)
  46. Chapter 7. Bio-Inspired Process Control (2/9)
  47. Chapter 7. Bio-Inspired Process Control (3/9)
  48. Chapter 7. Bio-Inspired Process Control (4/9)
  49. Chapter 7. Bio-Inspired Process Control (5/9)
  50. Chapter 7. Bio-Inspired Process Control (6/9)
  51. Chapter 7. Bio-Inspired Process Control (7/9)
  52. Chapter 7. Bio-Inspired Process Control (8/9)
  53. Chapter 7. Bio-Inspired Process Control (9/9)
  54. Chapter 8. Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms (1/4)
  55. Chapter 8. Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms (2/4)
  56. Chapter 8. Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms (3/4)
  57. Chapter 8. Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms (4/4)
  58. Chapter 9. Abstractions for Planning and Control of Robotic Swarms (1/4)
  59. Chapter 9. Abstractions for Planning and Control of Robotic Swarms (2/4)
  60. Chapter 9. Abstractions for Planning and Control of Robotic Swarms (3/4)
  61. Chapter 9. Abstractions for Planning and Control of Robotic Swarms (4/4)
  62. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (1/7)
  63. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (2/7)
  64. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (3/7)
  65. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (4/7)
  66. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (5/7)
  67. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (6/7)
  68. Chapter 10. Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks (7/7)
  69. Chapter 11. Human Peripheral Nervous System Controlling Robots (1/6)
  70. Chapter 11. Human Peripheral Nervous System Controlling Robots (2/6)
  71. Chapter 11. Human Peripheral Nervous System Controlling Robots (3/6)
  72. Chapter 11. Human Peripheral Nervous System Controlling Robots (4/6)
  73. Chapter 11. Human Peripheral Nervous System Controlling Robots (5/6)
  74. Chapter 11. Human Peripheral Nervous System Controlling Robots (6/6)
  75. Section III. BIO-INSPIRED COMMUNICATIONS AND NETWORKS
  76. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (1/10)
  77. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (2/10)
  78. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (3/10)
  79. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (4/10)
  80. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (5/10)
  81. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (6/10)
  82. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (7/10)
  83. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (8/10)
  84. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (9/10)
  85. Chapter 12. Adaptive Social Hierarchies: From Nature to Networks (10/10)
  86. Chapter 13. Chemical Relaying Protocols (1/4)
  87. Chapter 13. Chemical Relaying Protocols (2/4)
  88. Chapter 13. Chemical Relaying Protocols (3/4)
  89. Chapter 13. Chemical Relaying Protocols (4/4)
  90. Chapter 14. Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks (1/5)
  91. Chapter 14. Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks (2/5)
  92. Chapter 14. Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks (3/5)
  93. Chapter 14. Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks (4/5)
  94. Chapter 14. Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks (5/5)
  95. Chapter 15. Topological Robustness of Biological Systems for Information Networks—Modularity (1/4)
  96. Chapter 15. Topological Robustness of Biological Systems for Information Networks—Modularity (2/4)
  97. Chapter 15. Topological Robustness of Biological Systems for Information Networks—Modularity (3/4)
  98. Chapter 15. Topological Robustness of Biological Systems for Information Networks—Modularity (4/4)
  99. Chapter 16. Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks (1/4)
  100. Chapter 16. Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks (2/4)
  101. Chapter 16. Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks (3/4)
  102. Chapter 16. Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks (4/4)
  103. Chapter 17. Weakly Connected Oscillatory Networks for Information Processing (1/6)
  104. Chapter 17. Weakly Connected Oscillatory Networks for Information Processing (2/6)
  105. Chapter 17. Weakly Connected Oscillatory Networks for Information Processing (3/6)
  106. Chapter 17. Weakly Connected Oscillatory Networks for Information Processing (4/6)
  107. Chapter 17. Weakly Connected Oscillatory Networks for Information Processing (5/6)
  108. Chapter 17. Weakly Connected Oscillatory Networks for Information Processing (6/6)
  109. Chapter 18. Modeling the Dynamics of Cellular Signaling for Communication Networks (1/5)
  110. Chapter 18. Modeling the Dynamics of Cellular Signaling for Communication Networks (2/5)
  111. Chapter 18. Modeling the Dynamics of Cellular Signaling for Communication Networks (3/5)
  112. Chapter 18. Modeling the Dynamics of Cellular Signaling for Communication Networks (4/5)
  113. Chapter 18. Modeling the Dynamics of Cellular Signaling for Communication Networks (5/5)
  114. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (1/8)
  115. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (2/8)
  116. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (3/8)
  117. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (4/8)
  118. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (5/8)
  119. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (6/8)
  120. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (7/8)
  121. Chapter 19. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems (8/8)
  122. Back cover

Product information

  • Title: Bio-Inspired Computing and Networking
  • Author(s): Yang Xiao
  • Release date: April 2016
  • Publisher(s): CRC Press
  • ISBN: 9781420080339