期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
页码：1-8
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Traffic prediction; adaptive street lighting; smart
cities; energy efficient; network congestion
