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, 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-organizing data and signals cellular systems
Dynamic spectrum access in cognitive radio networks
QoS-aware architecture for scalable, adaptive, and survivable network systems
Locomotion control of the Hexapod Robot Gregor III
The book explores bio-inspired topology control and reconfiguration methods, as well as bio-inspired localization, synchronization, and mobility approaches. Providing wide-ranging coverage that includes past approaches, current challenges, and emerging concepts such as the evolution and self-healing of network architectures and protocols, this comprehensive reference provides you with the well-rounded understanding you need to continue the advancement of the development, design, and implementation of bio-inspired computing and networking.
Contents
ANIMAL BEHAVIORS AND ANIMAL COMMUNICATIONS
Animal Models for Computing and Communications: Past Approaches and Future Challenges; Karen L. Bales and Carolyn D. Kitzmann
Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale; Neil William Adams and Yang Xiao
NSPIRED COMPUTING AND ROBOTS
Social Insect Societies for the Optimization of Dynamic NP-Hard Problems; Stephan A. Hartmann, Pedro C. Pinto, Thomas A. Runkler, And João M.C. Sousa
Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III; Paolo Arena and Luca Patané
BEECLUST: A Swarm Algorithm Derived from Honeybees: Derivation of the Algorithm, Analysis by Mathematical Models, and Implementation on a Robot Swarm; Thomas Schmickl and Heiko Hamann
Self-Organizing Data and Signals Cellular Systems; André Stauffer and Gianluca Tempesti
Bio-Inspired Process Control; Konrad Wojdan, Konrad Swirski, Michalwarchol, Grzegorz Jarmoszewicz, And Tomasz Chomiak
Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms; Briana Wellman, Quinton Alexander, and Monica Anderson
Abstractions for Planning and Control of Robotic Swarms; Calin Belta
Ant-Inspired Allocation: Top-Down Controller Design for Distributing A Robot Swarm a...
