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Predictive Control for Distributed Smart Street Light Network

With the advent of smart city that embedded with smart technology, namely, smart streetlight, in urban development, the quality of living for citizens has been vastly improved. TALiSMaN is one of the promising smart streetlight schemes to date, however, it possesses certain limitation that led to ne...

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Main Authors:	Lau, Sei Ping, Tan, Chong Eng, Lee, Pei Zhen
Format:	Article
Language:	English
Published:	The Science and Information Organization 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://ir.unimas.my/id/eprint/28687/1/Predictive%20Control%20for%20Distributed%20Smart%20Street%20Light%20Network%20-%20Copy.pdf http://ir.unimas.my/id/eprint/28687/ https://thesai.org/Publications/ViewPaper?Volume=10&Issue=12&Code=IJACSA&SerialNo=44
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spelling	my.unimas.ir.286872024-01-09T03:15:15Z http://ir.unimas.my/id/eprint/28687/ Predictive Control for Distributed Smart Street Light Network Lau, Sei Ping Tan, Chong Eng Lee, Pei Zhen QA75 Electronic computers. Computer science With the advent of smart city that embedded with smart technology, namely, smart streetlight, in urban development, the quality of living for citizens has been vastly improved. TALiSMaN is one of the promising smart streetlight schemes to date, however, it possesses certain limitation 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 paper analyses three real-time short-term traffic prediction models, specifically simple moving average, exponential moving average and weighted moving average to be embedded onto TALiSMaN, that aim to ease network congestion. Additionally, the paper proposes traffic categorisation and packet propagation control mechanism that uses historical road traffic data to manage the network from overload. In this paper, we evaluate the performance of these models with TALiSMaN in simulated environment and compare them with TALiSMaN without traffic prediction model. Overall, weighted moving average showed promising results in reducing the packet dropped while capable of maintaining the usefulness of the streetlight when compared to TALiSMaN scheme, especially during rush hour The Science and Information Organization 2019 Article PeerReviewed text en http://ir.unimas.my/id/eprint/28687/1/Predictive%20Control%20for%20Distributed%20Smart%20Street%20Light%20Network%20-%20Copy.pdf Lau, Sei Ping and Tan, Chong Eng and Lee, Pei Zhen (2019) Predictive Control for Distributed Smart Street Light Network. (IJACSA) International Journal of Advanced Computer Science and Applications, 10 (12). pp. 328-335. ISSN 2156-5570 https://thesai.org/Publications/ViewPaper?Volume=10&Issue=12&Code=IJACSA&SerialNo=44 DOI : 10.14569/IJACSA.2019.0101244
institution	Universiti Malaysia Sarawak
building	Centre for Academic Information Services (CAIS)
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sarawak
content_source	UNIMAS Institutional Repository
url_provider	http://ir.unimas.my/
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Lau, Sei Ping Tan, Chong Eng Lee, Pei Zhen Predictive Control for Distributed Smart Street Light Network
description	With the advent of smart city that embedded with smart technology, namely, smart streetlight, in urban development, the quality of living for citizens has been vastly improved. TALiSMaN is one of the promising smart streetlight schemes to date, however, it possesses certain limitation 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 paper analyses three real-time short-term traffic prediction models, specifically simple moving average, exponential moving average and weighted moving average to be embedded onto TALiSMaN, that aim to ease network congestion. Additionally, the paper proposes traffic categorisation and packet propagation control mechanism that uses historical road traffic data to manage the network from overload. In this paper, we evaluate the performance of these models with TALiSMaN in simulated environment and compare them with TALiSMaN without traffic prediction model. Overall, weighted moving average showed promising results in reducing the packet dropped while capable of maintaining the usefulness of the streetlight when compared to TALiSMaN scheme, especially during rush hour
format	Article
author	Lau, Sei Ping Tan, Chong Eng Lee, Pei Zhen
author_facet	Lau, Sei Ping Tan, Chong Eng Lee, Pei Zhen
author_sort	Lau, Sei Ping
title	Predictive Control for Distributed Smart Street Light Network
title_short	Predictive Control for Distributed Smart Street Light Network
title_full	Predictive Control for Distributed Smart Street Light Network
title_fullStr	Predictive Control for Distributed Smart Street Light Network
title_full_unstemmed	Predictive Control for Distributed Smart Street Light Network
title_sort	predictive control for distributed smart street light network
publisher	The Science and Information Organization
publishDate	2019
url	http://ir.unimas.my/id/eprint/28687/1/Predictive%20Control%20for%20Distributed%20Smart%20Street%20Light%20Network%20-%20Copy.pdf http://ir.unimas.my/id/eprint/28687/ https://thesai.org/Publications/ViewPaper?Volume=10&Issue=12&Code=IJACSA&SerialNo=44
_version_	1787586513164304384
score	13.211869

Predictive Control for Distributed Smart Street Light Network

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