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Maximum Coverage and Flexible Convergence based 3-D Localization Approach for Wireless Sensor Networks
Objectives: Sensor network consists of sensor nodes, which independently organize the network and used in various applications like medical services, fire detection, military operations etc. This paper presents the 3-Dimensional GA based DV-Hop localization algorithm, which identifies the position of hidden nodes and distance from anchor node to the hidden node. The proposed model utilizes the swarm intelligence to localize the network. Methods: The proposed model simulation is entirely based upon the topology design with the randomly deployed network topology. The variable number of nodes has been obtained and the random positions are calculated to define the x and y coordinates of the nodes in the given topology. The proposed model has been tested with the different transmission ranges in order to understand the performance of the proposed model. Findings: The planned technique for localization empowers the wireless network to correctly analyze the solutions for the specific problems associated with the localization errors or positioning coverage. The performance parameters which compared with existing technique are average error and average localization error. Novelty of the Study: The experimental observations obtained from the proposed model shows the significant difference than the existing model, which shows the enhanced performance of planned model.
Genetic Algorithm, Localization Error Recovery, WSN Localization.
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