Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)

Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such...

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Main Authors: Hussain, Shaik Mazhar, Mohamad Yusof, Kamaludin
Format: Article
Language:English
Published: Engineering and Technology Publishing 2021
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Online Access:http://eprints.utm.my/id/eprint/97918/1/KamaludinMohamadYusof2021_DynamicQ-learningandFuzzyCNNBasedVerticalHandover.pdf
http://eprints.utm.my/id/eprint/97918/
http://dx.doi.org/10.12720/jcm.16.5.155-166
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spelling my.utm.979182022-11-10T01:01:57Z http://eprints.utm.my/id/eprint/97918/ Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV) Hussain, Shaik Mazhar Mohamad Yusof, Kamaludin TK Electrical engineering. Electronics Nuclear engineering Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss. Engineering and Technology Publishing 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/97918/1/KamaludinMohamadYusof2021_DynamicQ-learningandFuzzyCNNBasedVerticalHandover.pdf Hussain, Shaik Mazhar and Mohamad Yusof, Kamaludin (2021) Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV). Journal of Communications, 16 (5). pp. 155-166. ISSN 1796-2021 http://dx.doi.org/10.12720/jcm.16.5.155-166 DOI : 10.12720/jcm.16.5.155-166
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Hussain, Shaik Mazhar
Mohamad Yusof, Kamaludin
Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)
description Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss.
format Article
author Hussain, Shaik Mazhar
Mohamad Yusof, Kamaludin
author_facet Hussain, Shaik Mazhar
Mohamad Yusof, Kamaludin
author_sort Hussain, Shaik Mazhar
title Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)
title_short Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)
title_full Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)
title_fullStr Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)
title_full_unstemmed Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)
title_sort dynamic q-learning and fuzzy cnn based vertical handover decision for integration of dsrc, mmwave 5g and lte in internet of vehicles (iov)
publisher Engineering and Technology Publishing
publishDate 2021
url http://eprints.utm.my/id/eprint/97918/1/KamaludinMohamadYusof2021_DynamicQ-learningandFuzzyCNNBasedVerticalHandover.pdf
http://eprints.utm.my/id/eprint/97918/
http://dx.doi.org/10.12720/jcm.16.5.155-166
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