Description
Book SynopsisSeeking new methods to satisfy increasing communication demands, researchers continue to find inspiration from the complex systems found in nature. From ant-inspired allocation to a swarm algorithm derived from honeybees, Bio-Inspired Computing and Networking 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, including bio-inspired:
- Optimization of dynamic NP-hard problems
- Top-down controller design for distributing a robot swarm among multiple tasks
- Self-organ
Table of Contents
Animal Behaviors and Animal Communications. Animal Models for Computing and Communications: Past Approaches and Future Challenges. Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale. Bio-Inspired Computing and Robots. Social Insect Societies for the Optimization of Dynamic NP-Hard Problems. Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III. BEECLUST: A Swarm Algorithm Derived from Honeybees: Derivation of the Algorithm, Analysis by Mathematical Models, and Implementation on a Robot Swarm. Self-Organizing Data and Signals Cellular Systems. Bio-Inspired Process Control. Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms. Abstractions for Planning and Control of Robotic Swarms. Ant-Inspired Allocation: Top-Down Controller Design for Distributing A Robot Swarm among Multiple Tasks. Human Peripheral Nervous System Controlling Robots. Bio-Inspired Communications and Networks. Adaptive Social Hierarchies: From Nature to Networks. Chemical Relaying Protocols. Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks. Topological Robustness of Biological Systems for Information Networks—Modularity. Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks. Weakly Connected Oscillatory Networks for Information Processing. Modeling the Dynamics of Cellular Signaling for Communication Networks. A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems.
