SIT740 Research and Development: Intelligent Parking Literature

This literature review provides an analysis of four papers focusing on the development and implementation of intelligent parking systems (iParking). It begins with an introduction to the increasing problem of parking management due to the rising number of vehicles and limited parking spaces. The review covers key concepts such as the use of magnetic sensors, system components (sensor nodes, user interface, router, and server), and various detection methods including the use of 3-axis digital compass ICs. The comparison of the papers highlights different implementation approaches, including layered architectures, use case and class diagrams, and algorithms based on ATDA. The integration of cloud services and IoT for data storage and device management is also discussed, with one paper focusing on MQTT communication protocol. The review concludes that iParking systems have significantly improved conventional parking processes, achieving accuracy rates between 95.88% and 99%. It suggests future improvements should focus on increasing system accuracy and automation.

Running head: DEVELOPMENT OF INFORMATION TECHNOLOGY
Development of Information Technology in Intelligent Parking
Name of Student-
Name of University-
Author’s Note-

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Introduction
The application that is taken in this literature review is intelligent parking system. The
parking system is made with the emerging intelligent sensors, which helps to make the system
more efficient. The parking problem has increased in all the places where people need to park
their cars, and the parking in now a days has become a nuisance. According to Chen et al. (2016)
parking management is considered as a crucial task because of the increasing number of vehicles
and less amount of parking space. In this literature review, four papers are analyzed which gives
a detailed study of the Intelligent parking system.
Key Concepts
In one of the paper, the parking system that is described uses the magnetic sensor. The
cost of magnetic sensors is less compared to other sensors. The size of the magnetic sensors is
also much less than other sensors. In another paper, the system has four components including
the sensor nodes, user interface, router and a server (Zheng et al. 2015). There are many
detection method as well that are elaborated in the paper. To implement the iParking system,
authors uses 3-axis digital compass IC HMC5883L that is low in cost and gives the accurate
reading.
Comparison
The three papers describes different types of implementation of the iParking system. In
one of the article taken, there are different types of layer known as detection layer, access layer,
convergence layer an after passing through all those layers, the data is passed to the database.

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The network type can be of two types, public network and cable network. In one of the article
chosen for study, use case diagram and class diagram that illustrates the details of the parking
system. The methods that are described are very accurate and cost effective (Thai-Nghe and Chi-
Ngon 2014). In an article, different types of algorithms are defined to spot the parking that are
mostly based on ATDA algorithm (Adaptive Threshold Detection Algorithm). The cloud service
and the IoT services are also used by the automatic parking system for storing the data and
devices that are used (Dhar and Gupta 2016). The fourth paper designs a parking system which is
based on the principle of MQTT communication protocol.
Conclusion
From the above analysis, it can be stated that the system of automatic parking or iParking
has changed the process of conventional parking. The accuracy of the models that are described
in the papers is about 95.88 % to about 99 %. In future, to make the system much more better,
the accuracy of the systems can be increased and also the systems can be made fully automatic.

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References
Chen, N., Wang, L., Jia, L., Dong, H. and Li, H., 2016. Parking Survey Made Efficient in
Intelligent Parking Systems. Procedia engineering, 137, pp.487-495.
Dhar, P. and Gupta, P., 2016, September. Intelligent parking Cloud services based on IoT using
MQTT protocol. In Automatic Control and Dynamic Optimization Techniques (ICACDOT),
International Conference on (pp. 30-34). IEEE.
Thai-Nghe, N. and Chi-Ngon, N., 2014, December. An approach for building an intelligent
parking support system. In Proceedings of the Fifth Symposium on Information and
Communication Technology (pp. 192-201). ACM.
Zheng, D., Zhang, X., Shu, Y., Fang, C., Cheng, P. and Chen, J., 2015, April. iParking: An
intelligent parking system for large parking lots. In Computer Communications Workshops
(INFOCOM WKSHPS), 2015 IEEE Conference on (pp. 37-38). IEEE.