Introduction: Bio-inspired Systems -- Computer Networks -- Inceptive Finding -- Swarm Intelligence and Social Insects -- Immunology and Immune System -- Information Epidemics and Social Networking -- Artificial Neural Networks -- Genetic Algorithms -- Bio-inspired Software Defined Networking -- Case Study: Providing Trust in Wireless Sensor Networks -- Bio-inspired Approaches in Various Engineering Domain.

The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems in 2013; it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems in 2013; which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time in 2013; are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully applied in various domains. Nevertheless, it also presents a case study discussing the security aspects of wireless sensor networks and how biological solution stand out in comparison to optimized solutions. Furthermore, it also discusses novel biological solutions for solving problems in diverse engineering domains such as mechanical, electrical, civil, aerospace, energy and agriculture. The readers will not only get proper understanding of the bio inspired systems but also better insight for developing novel bio inspired solutions. Shows how bio-inspired systems in 2013; which are inherently robust, flexible and have high resilience towards critical errors -- hold immense potential for next generation network systems Outlines computing and problem solving techniques inspired by biological systems that can provide flexible, adaptable ways of solving networking problems Provides insights into how the study of biological systems can make network systems more flexible, adaptable, self-organized, self-aware, and self-sufficient.