Development of hybrid artificial intelligent based handover decision algorithm

The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligen...

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Main Authors:	Aibinu, Abiodun Musa, Onumanyi, Adeiza J., Adedigba, A. P., Ipinyomi, M., Folorunso, T. A., Salami, Momoh Jimoh Eyiomika
Format:	Article
Language:	English English
Published:	Elsevier B.V. 2017
Subjects:	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/57253/ http://irep.iium.edu.my/57253/ http://irep.iium.edu.my/57253/ http://irep.iium.edu.my/57253/2/57253_Development%20of%20hybrid%20artificial.htm http://irep.iium.edu.my/57253/3/57253_Development%20of%20hybrid%20artificial_SCOPUS.pdf

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spelling	iium-572532018-04-18T02:30:04Z http://irep.iium.edu.my/57253/ Development of hybrid artificial intelligent based handover decision algorithm Aibinu, Abiodun Musa Onumanyi, Adeiza J. Adedigba, A. P. Ipinyomi, M. Folorunso, T. A. Salami, Momoh Jimoh Eyiomika QA Mathematics TK Electrical engineering. Electronics Nuclear engineering The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN) based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS) was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k � step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k � step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k � step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques Elsevier B.V. 2017-04 Article NonPeerReviewed application/pdf en http://irep.iium.edu.my/57253/2/57253_Development%20of%20hybrid%20artificial.htm application/pdf en http://irep.iium.edu.my/57253/3/57253_Development%20of%20hybrid%20artificial_SCOPUS.pdf Aibinu, Abiodun Musa and Onumanyi, Adeiza J. and Adedigba, A. P. and Ipinyomi, M. and Folorunso, T. A. and Salami, Momoh Jimoh Eyiomika (2017) Development of hybrid artificial intelligent based handover decision algorithm. Engineering Science and Technology, an International Journal, 20 (2). pp. 381-390. E-ISSN 2215-0986 http://ac.els-cdn.com/S221509861631117X/1-s2.0-S221509861631117X-main.pdf?_tid=2b040d7e-4fd5-11e7-9174-00000aab0f6b&acdnat=1497316442_e2a8b6866a67d6a3cb95dbec3f12446d 10.1016/j.jestch.2017.01.005
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Aibinu, Abiodun Musa Onumanyi, Adeiza J. Adedigba, A. P. Ipinyomi, M. Folorunso, T. A. Salami, Momoh Jimoh Eyiomika Development of hybrid artificial intelligent based handover decision algorithm
description	The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN) based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS) was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k � step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k � step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k � step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques
format	Article
author	Aibinu, Abiodun Musa Onumanyi, Adeiza J. Adedigba, A. P. Ipinyomi, M. Folorunso, T. A. Salami, Momoh Jimoh Eyiomika
author_facet	Aibinu, Abiodun Musa Onumanyi, Adeiza J. Adedigba, A. P. Ipinyomi, M. Folorunso, T. A. Salami, Momoh Jimoh Eyiomika
author_sort	Aibinu, Abiodun Musa
title	Development of hybrid artificial intelligent based handover decision algorithm
title_short	Development of hybrid artificial intelligent based handover decision algorithm
title_full	Development of hybrid artificial intelligent based handover decision algorithm
title_fullStr	Development of hybrid artificial intelligent based handover decision algorithm
title_full_unstemmed	Development of hybrid artificial intelligent based handover decision algorithm
title_sort	development of hybrid artificial intelligent based handover decision algorithm
publisher	Elsevier B.V.
publishDate	2017
url	http://irep.iium.edu.my/57253/ http://irep.iium.edu.my/57253/ http://irep.iium.edu.my/57253/ http://irep.iium.edu.my/57253/2/57253_Development%20of%20hybrid%20artificial.htm http://irep.iium.edu.my/57253/3/57253_Development%20of%20hybrid%20artificial_SCOPUS.pdf
first_indexed	2023-09-18T21:20:54Z
last_indexed	2023-09-18T21:20:54Z
_version_	1777411874595274752

Development of hybrid artificial intelligent based handover decision algorithm

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