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A Novel Green IoT-Based Pay-As-You-Go Smart Parking System

Andrea Sant1, Lalit Garg1,*, Peter Xuereb1, Chinmay Chakraborty2

1 Faculty of Information & Communication Technology, University of Malta, Msida, Malta
2 Birla Institute of Technology, Mesra, Jharkhand, 814142, India

* Corresponding Author: Lalit Garg. Email:

(This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)

Computers, Materials & Continua 2021, 67(3), 3523-3544.


The better management of resources and the potential improvement in traffic congestion via reducing the orbiting time for parking spaces is crucial in a smart city, particularly those with an uneven correlation between the increase in vehicles and infrastructure. This paper proposes and analyses a novel green IoT-based Pay-As-You-Go (PAYG) smart parking system by utilizing unused garage parking spaces. The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’ pricing portfolio with a garage’s current demand. Malta, the world’s fourth-most densely populated country, is considered as a case of a smart city for the implementation of the proposed approach. The results obtained confirm that apart from having a high potential system in such countries, the pricing generated correctly forecasts the demand for a particular garage at a specific time of the day and year. The proposed PAYG smart parking system can effectively contribute to the macro solution to traffic congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.


Cite This Article

A. Sant, L. Garg, P. Xuereb and C. Chakraborty, "A novel green iot-based pay-as-you-go smart parking system," Computers, Materials & Continua, vol. 67, no.3, pp. 3523–3544, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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