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Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants

by Satvir Singh, Shivangna, Shelja Tayal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 22
Year of Publication: 2013
Authors: Satvir Singh, Shivangna, Shelja Tayal
10.5120/10768-5772

Satvir Singh, Shivangna, Shelja Tayal . Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants. International Journal of Computer Applications. 63, 22 ( February 2013), 31-37. DOI=10.5120/10768-5772

@article{ 10.5120/10768-5772,
author = { Satvir Singh, Shivangna, Shelja Tayal },
title = { Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 22 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number22/10768-5772/ },
doi = { 10.5120/10768-5772 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:11.331243+05:30
%A Satvir Singh
%A Shivangna
%A Shelja Tayal
%T Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 22
%P 31-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In a Wireless Sensor Network (WSN) accurate location of target node is highly desirable 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) algorithm and Particle Swarm Optimization (PSO) for distributed optimal localization of randomly deployed sensors for different ranges. 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 have only fast convergence but less mature. An investigation on distributed iterative localization is presented in this paper that shows how time consumption and error varies for different ranges. 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.

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Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization Biogeography Based Optimization Enhanced BBO Immigration Refusal Blended BBO Localization Wireless Sensor Networks

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