A novel RSSI prediction using imperialist competition algorithm (ICA), radial basis function (RBF) and firefly algorithm (FFA) in wireless networks

Goudarzi, Shidrokh and Hassan, Wan Haslina and Hassan Abdalla Hashim, Aisha and Soleymani, Seyed Ahmad and Anisi, Mohammad Hossein and Zakaria, Omar M. (2016) A novel RSSI prediction using imperialist competition algorithm (ICA), radial basis function (RBF) and firefly algorithm (FFA) in wireless networks. PLOS ONE, 11 (7). e0151355-1. ISSN 1932-6203

	PDF - Published Version Restricted to Registered users only Download (3MB) | Request a copy
	PDF (scopus) - Supplemental Material Restricted to Repository staff only Download (327kB) | Request a copy
	PDF (wos) - Supplemental Material Restricted to Repository staff only Download (141kB) | Request a copy

Abstract

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2 ), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

Item Type:	Article (Journal)
Additional Information:	2523/51495
Uncontrolled Keywords:	mobile node (MN), RSSI Prediction, Competition Algorithm (ICA), Radial Basis Function (RBF)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Aisha Hassan Abdalla Hashim
Date Deposited:	11 Aug 2016 13:32
Last Modified:	20 Oct 2017 22:42
URI:	http://irep.iium.edu.my/id/eprint/51495

Actions (login required)

View Item