Smart Car Parking System Solution for the Internet of Things in Smart Cities

被引:0
作者
Alsafery, Wael [1 ]
Alturki, Badraddin [1 ]
Reiff-Marganiec, Stephan [1 ]
Jambi, Kamal [2 ]
机构
[1] Univ Leicester, Dept Informat, Leicester, Leics, England
[2] King Abdulaziz Univ, Comp Sci Dept, Jeddah, Saudi Arabia
来源
2018 1ST INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS & INFORMATION SECURITY (ICCAIS' 2018) | 2018年
关键词
Internet of Things (IoT); smart city; smart car parking; Disruptive Car Parks; embedded computing; Cloud data mining; Fog Computing; Distributed Data Analytics;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.
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页数:5
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