Bio-inspired NetworkingBio-inspired techniques are based on principles, or models, of biological systems. In general, natural systems present remarkable capabilities of resilience and adaptability. In this book, we explore how bio-inspired methods can solve different problems linked to computer networks.Future networks are expected to be autonomous, scalable and adaptive. During millions of years of evolution, nature has developed a number of different systems that present these and other characteristics required for the next generation networks. Indeed, a series of bio-inspired methods have been successfully used to solve the most diverse problems linked to computer networks. This book presents some of these techniques from a theoretical and practical point of view.
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adapted agents alleles ants applied artificial Artificial chemistries attack autonomic computing backpropagation basic behavior bio-inspired biological systems brain cell characteristics chemical chromosomes cluster complex composed computer networks concentration convergence cortex create defined dynamics efficient energy environment epigenesis error evaluation evolution example fitness function fraglets genes genetic algorithms genetic material global heuristic hidden layers hoc networks IEEE individual input inspired interaction intrusion detection system learning linked MapReduce metaheuristic method molecules multicast mutation natural systems neighbors neocortex nervous system network lifetime neural networks neurons NP-complete objective offspring organism output layer packet parameters particle swarm optimization path perceptron pheromone population position present problem Proceedings proposed protocol random randomly reactants reaction relay node represents reproduction result routing algorithm selection signals solution solve specific Stigmergy structure swarm intelligence synapses techniques traits update weights wireless sensor networks