Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network

This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoe network (VANET) by optimising the network coding (NC) using Genetic Algorithm and Particle Swarm Optimisation (GA-PSO). VANET shows a promising technology as it could enhance the traffic efficiency and p...

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Main Author:	Lee, Chun Hoe
Format:	Thesis
Language:	English English
Published:	2017
Subjects:	TK5101-6720 Telecommunication Including telegraphy telephone radio radar television
Online Access:	https://eprints.ums.edu.my/id/eprint/38880/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38880/2/FULLTEXT.pdf
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id	my-ums-ep.38880
record_format	uketd_dc
spelling	my-ums-ep.388802024-06-18T02:14:59Z Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network 2017 Lee, Chun Hoe TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoe network (VANET) by optimising the network coding (NC) using Genetic Algorithm and Particle Swarm Optimisation (GA-PSO). VANET shows a promising technology as it could enhance the traffic efficiency and promote traffic safety on the road systems. The conventional store-and-forward transmission protocol used in the intermediate node(s) simply stores the received packet and then send at a later time to the destination. However, the rapid changing in VANET topology has made the conventional store-and-forward approach inefficient to meet the throughput and reliability demand posed by VANET. Hence, NC is proposed to perform additional functions on the packet in the source or intermediate node(s). The results showed that the NC used in wireless network outperforms the conventional store-and-forward in terms of throughput and energy consumption. However, the chances to perform NC in wireless network is highly unlikely if the packet is not transmit to the potential NC node. Therefore, GA based network routing (GANeR) is embedded into network to search for shortest path from the source to the destination, and PSO based coding aware routing (CAR) is also proposed to further converge the solutions obtained from GANeR. It showed that the developed GA-PSO in this work provides a better route with coding opportunities and reduces energy consumption in the network. The total energy consumed by GA-PSO is 7.39% fewer than the store-and-forward approach and 4. 77% fewer than NC in wireless network transmission and forwarding structure (COPE). 2017 Thesis https://eprints.ums.edu.my/id/eprint/38880/ https://eprints.ums.edu.my/id/eprint/38880/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/38880/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah Fakulti Kejuruteraan
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TK5101-6720 Telecommunication Including telegraphy telephone radio radar television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy telephone radio radar television Lee, Chun Hoe Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
description	This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoe network (VANET) by optimising the network coding (NC) using Genetic Algorithm and Particle Swarm Optimisation (GA-PSO). VANET shows a promising technology as it could enhance the traffic efficiency and promote traffic safety on the road systems. The conventional store-and-forward transmission protocol used in the intermediate node(s) simply stores the received packet and then send at a later time to the destination. However, the rapid changing in VANET topology has made the conventional store-and-forward approach inefficient to meet the throughput and reliability demand posed by VANET. Hence, NC is proposed to perform additional functions on the packet in the source or intermediate node(s). The results showed that the NC used in wireless network outperforms the conventional store-and-forward in terms of throughput and energy consumption. However, the chances to perform NC in wireless network is highly unlikely if the packet is not transmit to the potential NC node. Therefore, GA based network routing (GANeR) is embedded into network to search for shortest path from the source to the destination, and PSO based coding aware routing (CAR) is also proposed to further converge the solutions obtained from GANeR. It showed that the developed GA-PSO in this work provides a better route with coding opportunities and reduces energy consumption in the network. The total energy consumed by GA-PSO is 7.39% fewer than the store-and-forward approach and 4. 77% fewer than NC in wireless network transmission and forwarding structure (COPE).
format	Thesis
qualification_level	Master's degree
author	Lee, Chun Hoe
author_facet	Lee, Chun Hoe
author_sort	Lee, Chun Hoe
title	Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
title_short	Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
title_full	Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
title_fullStr	Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
title_full_unstemmed	Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
title_sort	evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network
granting_institution	Universiti Malaysia Sabah
granting_department	Fakulti Kejuruteraan
publishDate	2017
url	https://eprints.ums.edu.my/id/eprint/38880/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38880/2/FULLTEXT.pdf
_version_	1804890344981004288

Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network

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