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Nature-Inspired Computing for Autonomic Wireless Sensor Networks

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2 Author(s)

This chapter presents how nature-inspired algorithms can affect the underlying design principles, enabling technologies, implementation, and management issues of autonomic wireless sensor networks (WSNs). It explains how WSN can benefit from autonomic mechanisms. The chapter provides a brief introduction to nature-inspired computing. It reviews several current nature-inspired techniques such as genetic algorithms (GA) and genetic programming (GP) to construct autonomic WSN. The chapter also presents the state-of-the-art techniques in those areas. It introduces several works such as bio-networking architecture, and biologically-inspired architecture for sensor NETworks (BiSNET) in which multiple biological principles are used to build fundamental frameworks for implementing autonomic WSNs. Self-organized mechanisms alleviate the dependence of sensors on a central controller and therefore help sensors to consume energy more efficiently in reaching the primary goal of WSNs to prolong their lifetime.

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