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A New Fuzzy Based Localization Error Minimization Approach with Optimized Beacon Range

Published on None 2011 by Vinay Kuma, Ashok Kumar, Surender Soni
journal_cover_thumbnail

International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 6
None 2011
Authors: Vinay Kuma, Ashok Kumar, Surender Soni
75413d35-ab0b-4a13-8f98-8f8c02498850

Vinay Kuma, Ashok Kumar, Surender Soni . A New Fuzzy Based Localization Error Minimization Approach with Optimized Beacon Range. International Conference and Workshop on Emerging Trends in Technology. ICWET, 6 (None 2011), 52-59.

@article{
author = { Vinay Kuma, Ashok Kumar, Surender Soni },
title = { A New Fuzzy Based Localization Error Minimization Approach with Optimized Beacon Range },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 6 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 52-59 },
numpages = 8,
url = { /proceedings/icwet/number6/2104-ce106/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Vinay Kuma
%A Ashok Kumar
%A Surender Soni
%T A New Fuzzy Based Localization Error Minimization Approach with Optimized Beacon Range
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 6
%P 52-59
%D 2011
%I International Journal of Computer Applications
Abstract

One of the fundamental problems in wireless sensor networks (WSNs) is localization that forms the basis for many location aware applications. Localization in WSNs is to determine the physical position of sensor node based on the known positions of several nodes. In this paper, a range free, enhanced weighted centroid localization method using edge weights of adjacent nodes is proposed. In the proposed method, first the adjacent reference (anchor) nodes which are connected to the node to be localized are found, and then the edge weights based on received signal strength indicator information (RSSI) using Mamdani and Sugeno fuzzy inference systems are calculated. After localizing the sensor node by weighted centroid formula using both the Mamdani and Sugeno fuzzy system, a combined approach to localize the node is employed. Finally, the proposed method is simulated to demonstrate the performance by comparing them with the simple centroid, individual Mamdani and Sugeno fuzzy method. Location accuracy is further enhanced by calculating the optimized beacons transmission range to minimize the localization error.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Centroid localization Edge weight Fuzzy logic system Range-free localization Wireless Sensor Networks