Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes

In recent years, there has been a growing interest in localization for wireless sensor networks. Since the complex behavior of such network, various machine learning-based methods are proposed in order to improve localization goals. The objective of this paper is to compare three well known learning...

Full description

Saved in:
Bibliographic Details
Published inInternational Wireless Communications and Mobile Computing Conference (Online) pp. 1554 - 1558
Main Authors Ahmadi, Hanen, Bouallegue, Ridha
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2015
Subjects
Artificial neural networks
Complexity theory
Localization
Machine Learning
Naïve Bayes
Neural Network
Niobium
Support vector machines
Support Vector Regression
Training
Wireless sensor networks
WSN
Online AccessGet full text
ISSN2376-6492
DOI10.1109/IWCMC.2015.7289314

Cover

More Information
Summary:In recent years, there has been a growing interest in localization for wireless sensor networks. Since the complex behavior of such network, various machine learning-based methods are proposed in order to improve localization goals. The objective of this paper is to compare three well known learning-based localization techniques using Received Signal Strength Indicator (RSSI): the Support Vector regression, Naïve Bayes and Artificial Neural Network. We take into consideration two performance keys: the localization error and the computation complexity.
ISSN:2376-6492
DOI:10.1109/IWCMC.2015.7289314