Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suita...

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Autores principales:	Hussain Shaik Mazhar, Yusof Kamaludin Mohamad, Hussain Shaik Ashfaq
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2021
Materias:	internet of vehicles (iov) 4g lte 5g mm-wave dsrc vertical handover and network selection Transportation and communication K4011-4343
Acceso en línea:	https://doaj.org/article/d927d45eee594d1e92921bcaf5c25342
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id	oai:doaj.org-article:d927d45eee594d1e92921bcaf5c25342
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spelling	oai:doaj.org-article:d927d45eee594d1e92921bcaf5c253422021-12-05T14:11:11ZArtificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles1407-617910.2478/ttj-2021-0030https://doaj.org/article/d927d45eee594d1e92921bcaf5c253422021-11-01T00:00:00Zhttps://doi.org/10.2478/ttj-2021-0030https://doaj.org/toc/1407-6179Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.Hussain Shaik MazharYusof Kamaludin MohamadHussain Shaik AshfaqSciendoarticleinternet of vehicles (iov)4g lte5g mm-wavedsrcvertical handover and network selectionTransportation and communicationK4011-4343ENTransport and Telecommunication, Vol 22, Iss 4, Pp 392-406 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	internet of vehicles (iov) 4g lte 5g mm-wave dsrc vertical handover and network selection Transportation and communication K4011-4343
spellingShingle	internet of vehicles (iov) 4g lte 5g mm-wave dsrc vertical handover and network selection Transportation and communication K4011-4343 Hussain Shaik Mazhar Yusof Kamaludin Mohamad Hussain Shaik Ashfaq Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles
description	Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.
format	article
author	Hussain Shaik Mazhar Yusof Kamaludin Mohamad Hussain Shaik Ashfaq
author_facet	Hussain Shaik Mazhar Yusof Kamaludin Mohamad Hussain Shaik Ashfaq
author_sort	Hussain Shaik Mazhar
title	Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles
title_short	Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles
title_full	Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles
title_fullStr	Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles
title_full_unstemmed	Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles
title_sort	artificial intelligence-based network selection and optimized routing in internet of vehicles
publisher	Sciendo
publishDate	2021
url	https://doaj.org/article/d927d45eee594d1e92921bcaf5c25342
work_keys_str_mv	AT hussainshaikmazhar artificialintelligencebasednetworkselectionandoptimizedroutingininternetofvehicles AT yusofkamaludinmohamad artificialintelligencebasednetworkselectionandoptimizedroutingininternetofvehicles AT hussainshaikashfaq artificialintelligencebasednetworkselectionandoptimizedroutingininternetofvehicles
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Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

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