Mapping Biological Systems to Network SystemsThe book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – 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 – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – 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. |
Contents
| 1 | |
2 Computer Networks | 11 |
3 Inceptive Findings | 27 |
4 Swarm Intelligence and Social Insects | 36 |
5 Immunology and Immune System | 51 |
6 Information Epidemics and Social Networking | 67 |
7 Artificial Neural Network | 79 |
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Common terms and phrases
activation adaptive algorithm analysis antibodies antigen applications approach Artificial artificial immune attack B-cells behavior bio-inspired biological body calculated cells chapter classification clonal selection clusters communication compared complex components Conference connected depends detection disease effective efficient energy engineering equation error et al example fault frameworks fraudulent nodes function given gives human IEEE immune system increase input inspired intelligence International issues Journal layer learning machine Mapping measurements mechanism method natural network systems neural network neurons optimization organisms output packet path pattern performed physical present problem Proceedings produce protocol provides represented response routing rule selection sensor nodes shown in Fig signal social networks Soil solution specific spread structure techniques technologies topology trust utilized various weights wireless sensor networks