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

Goudarzi, S. and Hassan, W. H. and Hashim, A. H. A. and Soleymani, S. A. and Anisi, M. H. and Zakaria, O. 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). ISSN 1932-6203

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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 models 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
Uncontrolled Keywords:competition, correlation coefficient, firefly, genetic polymorphism, model, perceptron, prediction, support vector machine, algorithm, artificial neural network, theoretical model, wireless communication, Algorithms, Models, Theoretical, Neural Networks (Computer), Support Vector Machine, Wireless Technology
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:72362
Deposited By: Narimah Nawil
Deposited On:20 Nov 2017 08:23
Last Modified:20 Nov 2017 08:23

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