Research on Node Localization Algorithm in WSN basing Machine Learning

DOI

10.2991/iccia.2012.10How to use a DOI?

Keywords

wireless sensor network, node localization, support vector machine, region classification, coverage hole

Abstract

Machine learning uses experience to improve its performance. Using Machine Learing, to locate the nodes in wireless sensor network. The basic idea is that: the network area is divided into several equal portions of small grids, each gird represents a certain class of Machine Learning algorithm. After Machine Learning algorithm has learnt the parameters using the known beacon nodes, it can classify the unknown nodes’ location classes, and further determine their coordinates. For the SVM OneAgainstOne Location Algorithm, the results of simulation show that it has a high localization accuracy and a better tolerance for the ranging error, while it doesn’t require a high beacon node ratio. For the SVM Decision Tree Location Algorithm, the results show that this algorithm is not affected seriously by coverage holes, it is suitable for the network environment of nonuniformity distribution or existing coverage holes.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qingzhang Chen
AU  - Yuzheng Chen
AU  - Congling Fan
AU  - Fan Yang
AU  - Peng Wang
PY  - 2014/05
DA  - 2014/05
TI  - Research on Node Localization Algorithm in WSN basing Machine Learning
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 43
EP  - 46
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.10
DO  - 10.2991/iccia.2012.10
ID  - Chen2014/05
ER  -