Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks

With the advent of a smart city that is embedded with smart technology, namely smart streetlight, in urban development, the quality of living for citizens has been vastly improved. Traffic-Aware Street lighting Scheme Management Network (TALiSMaN) is one of the apt smart streetlight schemes to date;...

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Main Author:	Pei Zhen, Lee
Format:	Thesis
Language:	English English
Published:	2021
Subjects:	QA75 Electronic computers Computer science QA76 Computer software
Online Access:	http://ir.unimas.my/id/eprint/36569/1/Lee%20Pei%20Zhen%20-%2024%20pgs.pdf http://ir.unimas.my/id/eprint/36569/3/Lee%20Pei%20Zhen%20ft.pdf
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id	my-unimas-ir.36569
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spelling	my-unimas-ir.365692023-06-21T07:53:00Z Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks 2021 Pei Zhen, Lee QA75 Electronic computers. Computer science QA76 Computer software TE Highway engineering. Roads and pavements With the advent of a smart city that is embedded with smart technology, namely smart streetlight, in urban development, the quality of living for citizens has been vastly improved. Traffic-Aware Street lighting Scheme Management Network (TALiSMaN) is one of the apt smart streetlight schemes to date; however, it possesses certain limitations that led to network congestion and packet dropped during peak road traffic periods. Traffic prediction is vital in network management, especially for real-time decision-making and latency-sensitive application. With that in mind, this work analyses three low-complexity real-time prediction techniques, namely simple moving average, exponential moving average, and weighted moving average, to be embedded onto TALiSMaN, which aims to ease the network congestion. Additionally, this work proposes traffic categorisation and packet propagation control mechanism that uses historical road traffic data to manage the network from overload. The performance of these prediction techniques with TALiSMaN was simulated and compared with the original TALiSMaN scheme. Overall, the simple moving average showed promising results in reducing the packet dropped by 12.9% – 37.4% while capable of improving up to 2.9% of the streetlight usefulness experienced by the road users, when compared to the original TALiSMaN scheme, especially during rush hour. Universiti Malaysia Sarawak (UNIMAS) 2021 Thesis http://ir.unimas.my/id/eprint/36569/ http://ir.unimas.my/id/eprint/36569/1/Lee%20Pei%20Zhen%20-%2024%20pgs.pdf text en public http://ir.unimas.my/id/eprint/36569/3/Lee%20Pei%20Zhen%20ft.pdf text en validuser masters Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English
topic	QA75 Electronic computers Computer science QA76 Computer software QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science QA76 Computer software QA75 Electronic computers Computer science Pei Zhen, Lee Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks
description	With the advent of a smart city that is embedded with smart technology, namely smart streetlight, in urban development, the quality of living for citizens has been vastly improved. Traffic-Aware Street lighting Scheme Management Network (TALiSMaN) is one of the apt smart streetlight schemes to date; however, it possesses certain limitations that led to network congestion and packet dropped during peak road traffic periods. Traffic prediction is vital in network management, especially for real-time decision-making and latency-sensitive application. With that in mind, this work analyses three low-complexity real-time prediction techniques, namely simple moving average, exponential moving average, and weighted moving average, to be embedded onto TALiSMaN, which aims to ease the network congestion. Additionally, this work proposes traffic categorisation and packet propagation control mechanism that uses historical road traffic data to manage the network from overload. The performance of these prediction techniques with TALiSMaN was simulated and compared with the original TALiSMaN scheme. Overall, the simple moving average showed promising results in reducing the packet dropped by 12.9% – 37.4% while capable of improving up to 2.9% of the streetlight usefulness experienced by the road users, when compared to the original TALiSMaN scheme, especially during rush hour.
format	Thesis
qualification_level	Master's degree
author	Pei Zhen, Lee
author_facet	Pei Zhen, Lee
author_sort	Pei Zhen, Lee
title	Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks
title_short	Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks
title_full	Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks
title_fullStr	Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks
title_full_unstemmed	Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks
title_sort	prediction based lighting control scheme for wireless managed streetlight networks
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2021
url	http://ir.unimas.my/id/eprint/36569/1/Lee%20Pei%20Zhen%20-%2024%20pgs.pdf http://ir.unimas.my/id/eprint/36569/3/Lee%20Pei%20Zhen%20ft.pdf
_version_	1783728475499134976

Prediction based Lighting Control Scheme for Wireless Managed Streetlight Networks

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