Bio-inspired Networking by Daniel Câmara
By Daniel Câmara
Bio-inspired concepts are according to ideas, or versions, of organic structures. ordinarily, traditional structures current extraordinary features of resilience and flexibility. during this e-book, we discover how bio-inspired equipment can resolve diverse difficulties associated with desktop networks.
Future networks are anticipated to be self reliant, scalable and adaptive. in the course of hundreds of thousands of years of evolution, nature has built a few assorted platforms that current those and different features required for the following iteration networks. certainly, a chain of bio-inspired tools were effectively used to unravel the main various difficulties associated with desktop networks. This e-book offers a few of these strategies from a theoretical and functional aspect of view.
- Discusses the foremost ideas of bio-inspired networking to help you to find effective networking solutions
- Delivers examples of innovations either in theoretical options and sensible applications
- Helps you observe nature's dynamic source and activity administration for your desktop networks
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The fitness function takes into account the nodes’ residual energy and the expected cost in energy for a given route scheme, defined on each of the individual’s chromosome. Evolution and Evolutionary Algorithms 23 It is common that over the network lifetime, different relay nodes present different residual energy levels. Even if the routing algorithm is correct and balances the charges well, no routing scheme is resilient to unforeseen events, such as node failures for example. In [BAR 09], new routing schemes are started periodically, but they are also triggered by network failures.
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For representing a 25-node network, each gene requires 3 × 25 = 75 bits. For example, the representation for the first four nodes, where the first node is a cluster head, the third node is an inter-cluster router and the other two are sensor nodes, would be “001 100 010 100”. The genetic operations for node selection are Evolution and Evolutionary Algorithms 15 performed over this representation. The fitness function is a weighted function that takes into account four aspects linked to the cluster formation.