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Reseach Article

Range-free Sensor Positioning based on Bacterial Foraging Algorithm (BFO) in Wireless Sensor Networks

by N. Pushpalatha, B.anuradha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 9
Year of Publication: 2015
Authors: N. Pushpalatha, B.anuradha

N. Pushpalatha, B.anuradha . Range-free Sensor Positioning based on Bacterial Foraging Algorithm (BFO) in Wireless Sensor Networks. International Journal of Computer Applications. 122, 9 ( July 2015), 35-40. DOI=10.5120/21731-4901

@article{ 10.5120/21731-4901,
author = { N. Pushpalatha, B.anuradha },
title = { Range-free Sensor Positioning based on Bacterial Foraging Algorithm (BFO) in Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 9 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { },
doi = { 10.5120/21731-4901 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:10:09.154982+05:30
%A N. Pushpalatha
%A B.anuradha
%T Range-free Sensor Positioning based on Bacterial Foraging Algorithm (BFO) in Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 9
%P 35-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

In Wireless Sensor Network (WSN), the existing sensor positioning technique may result in increased cost, energy consumption, connectivity failure and less accuracy. In order to overcome these issues, in this paper, we propose a range-free sensor positioning based on Bacterial Foraging Algorithm (BFO) in WSN. In this technique, initially the anchor nodes are placed using the coverage ratio. The coverage ratio depends on the network size. Then the anchor nodes use the BFO algorithm to estimate the distance between the unknown sensor nodes using neighbor density. BFO is a computational intelligence based technique that is not largely affected by the size and nonlinearity of the problem and can converge to the optimal solution in many problems where most analytical methods fail to converge. By simulation results, we show that the proposed technique enhances the accuracy and reduces the energy consumption.

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Index Terms

Computer Science
Information Sciences


WSN BFOA Energy Consumption Accuracy