Seeking 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, networki
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.