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Range Based Wireless Sensor Node Localization Using PSO and BBO and Its Variants

Accurate location of target node is highly desirable in a Wireless Sensor Network (WSN) as it has strong impact on overall performance of the WSN. This paper proposes the application of different migration variants of Biogeography-Based Optimization (BBO) algorithms and Particle Swarm Optimization (PSO) for distributed optimal localization of randomly deployed sensors. Biogeography is collective learning of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i.e., problem solution. PSO models had only fast convergence but less mature. An investigation on distributed iterative localization is presented in this paper. Here the nodes that get localized in iteration act as anchor node. A comparison of the performance of PSO and different migration variants of BBO in terms of number of nodes localized, localization accuracy and computation time is presented.