Applied Mathematics & Information Sciences

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Energy efficiency is of utmost importance in Wireless Sensor Networks (WSN). A WSN that lives long proves to be more worthy for the effort and cost involved in establishing the network. The standards and protocols in aWSN are expected to be economical in their energy usage. Localization is one such important area, where much contribution has been made by the researchers to improve the accuracy and not much in increasing their energy efficiency. In this work, a hierarchical approach to increase energy efficiency during localization has been proposed. Existing localization approaches, irrespective of the node’s remaining energy levels, use a common strategy for all the nodes. To balance the energy usage among nodes, the proposed algorithm uses different strategies based on current energy level. Clustering is performed as part of localization and the cluster heads also serve as localization heads for their cluster members. Range estimation is done using fuzzy system and localization is performed by genetic simulated annealing. Simulation has been carried out in NS2 and the

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