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

This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoc 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
Published:	2017
Online Access:	https://eprints.ums.edu.my/id/eprint/26668/1/Evolutionary%20algorithm%20based%20network%20coding%20for%20optmization%20of%20intelligent%20vehicular%20ad%20hoc%20network.pdf
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spelling	my-ums-ep.266682021-01-21T06:45:04Z Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network 2017 Lee, Chun Hoe This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoc 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/26668/ https://eprints.ums.edu.my/id/eprint/26668/1/Evolutionary%20algorithm%20based%20network%20coding%20for%20optmization%20of%20intelligent%20vehicular%20ad%20hoc%20network.pdf text en validuser mphil masters Universiti Malaysia Sabah Faculty of Engineering
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English
description	This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoc 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_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Lee, Chun Hoe
spellingShingle	Lee, Chun Hoe Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
author_facet	Lee, Chun Hoe
author_sort	Lee, Chun Hoe
title	Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
title_short	Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
title_full	Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
title_fullStr	Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
title_full_unstemmed	Evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
title_sort	evolutionary algorithm based network coding for optmization of intelligent vehicular ad hoc network
granting_institution	Universiti Malaysia Sabah
granting_department	Faculty of Engineering
publishDate	2017
url	https://eprints.ums.edu.my/id/eprint/26668/1/Evolutionary%20algorithm%20based%20network%20coding%20for%20optmization%20of%20intelligent%20vehicular%20ad%20hoc%20network.pdf
_version_	1747836559466430464

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

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