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QoS Performance Analysis of Application Layer for LTE C-V2X, Real-Time V2X Communication, and Applications of Machine Learning-Based Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	QoS Performance Analysis of Application Layer for LTE C-V2X, Real-Time V2X Communication, and Applications of Machine Learning-Based Techniques./
Author:	Awad, Ahmed.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	111 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
Subject:	Communication. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28495398
ISBN:	9798516912467

QoS Performance Analysis of Application Layer for LTE C-V2X, Real-Time V2X Communication, and Applications of Machine Learning-Based Techniques.
Awad, Ahmed.

QoS Performance Analysis of Application Layer for LTE C-V2X, Real-Time V2X Communication, and Applications of Machine Learning-Based Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 111 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--University of Arkansas at Little Rock, 2021.

This item is not available from ProQuest Dissertations & Theses.

The field of connected vehicles (CV) stands at the confluence of three evolving disciplines - the Internet of Things (IoT), emerging standards for connectivity of vehicles, and AI/machine learning. CV regarded as a true solution to reduce the number of accidents. in this work conducting a protocol performance road test and applying machine learning algorithms have been investigated. we used Universal Software Radio Peripheral (USRP) N210 devices to build ad hoc Vehicles Network based on IEEE 802.11p protocol and create an application layer using python language to build drowsiness detection system which resolve a significant safety issue nowadays. The application layer has been built based on MAC and Physical layers to deliver a real time drowsiness detection system, which determined the rate of messages that can be transmitted and received in both indoor and outdoor environments. The response time was observed to be around 50 seconds on average. Our aim is to implement DSRC based on IEEE 802.11p protocol so the connected vehicles can share data between themselves in real-time. Besides, we investigate scalability analysis for database traffic rate for IMS -eMBMS (evolved multi broadcast multicast services) servers of CV2X Networks to provide network designers with a vision into size IMS-eMBMS servers that required to support vehicle users by determining the overall load on the cellular system.

ISBN: 9798516912467Subjects--Topical Terms:

524709
Communication.
Subjects--Index Terms:

CV2X

QoS Performance Analysis of Application Layer for LTE C-V2X, Real-Time V2X Communication, and Applications of Machine Learning-Based Techniques.
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245 1 0 $a QoS Performance Analysis of Application Layer for LTE C-V2X, Real-Time V2X Communication, and Applications of Machine Learning-Based Techniques.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 111 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
500 $a Advisor: Mohan, Seshadri.
502 $a Thesis (Ph.D.)--University of Arkansas at Little Rock, 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a The field of connected vehicles (CV) stands at the confluence of three evolving disciplines - the Internet of Things (IoT), emerging standards for connectivity of vehicles, and AI/machine learning. CV regarded as a true solution to reduce the number of accidents. in this work conducting a protocol performance road test and applying machine learning algorithms have been investigated. we used Universal Software Radio Peripheral (USRP) N210 devices to build ad hoc Vehicles Network based on IEEE 802.11p protocol and create an application layer using python language to build drowsiness detection system which resolve a significant safety issue nowadays. The application layer has been built based on MAC and Physical layers to deliver a real time drowsiness detection system, which determined the rate of messages that can be transmitted and received in both indoor and outdoor environments. The response time was observed to be around 50 seconds on average. Our aim is to implement DSRC based on IEEE 802.11p protocol so the connected vehicles can share data between themselves in real-time. Besides, we investigate scalability analysis for database traffic rate for IMS -eMBMS (evolved multi broadcast multicast services) servers of CV2X Networks to provide network designers with a vision into size IMS-eMBMS servers that required to support vehicle users by determining the overall load on the cellular system.
590 $a School code: 1204.
650 4 $a Communication. $3 524709
650 4 $a Multimedia communications. $3 590562
650 4 $a Computer engineering. $3 621879
650 4 $a Artificial intelligence. $3 516317
650 4 $a Electrical engineering. $3 649834
653 $a CV2X
653 $a Dedicated short range communications
653 $a LTE-A Pro
653 $a Machine learning
653 $a Connected vehicles
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790 $a 1204
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28495398