MATEC Web Conf.
Volume 164, 2018The 3rd International Conference on Electrical Systems, Technology and Information (ICESTI 2017)
|Number of page(s)||11|
|Published online||23 April 2018|
IoT-Based Car's Parking Monitoring System
Electrical Engineering Department, Faculty of Industrial Technology, Petra Christian University, Jl. Siwalankerto 121-131, Surabaya, 60234, Indonesia
2 Informatics Department, Faculty of Information Technology, Institut Informatika Indonesia, Jl. Pattimura 3, Surabaya, 60189, Indonesia
* Corresponding author: firstname.lastname@example.org
Internet-of-things-based technologies have advanced so much and helped public necessities. The use of IoT at a parking lot will help vehicle users to know the availability of a parking location through smartphones. This IoT-based parking system is created by using controllers, sensors, servers and cloud. Controllers and sensors will be placed on the ceiling of each parking slots to detect the presence of a car. Server collect the results of the sensors and store them in Cloud. System test is conducted by installing three sensor circuits and server in a parking lot. The tests consist of measuring time that required for data transmission and the rate of success of data transmission from the parking lot to the Cloud. Based on above tests, it is observed that the sensor circuit and Radio Frequency Identification are able to transmit the parking lot data without error. This system require maximum 1 min to update parking lot data. The process of obtaining data until the data being stored in Cloud takes 12 s and the process of acquiring parking condition data from Cloud to smartphone takes 30 s. The accuracy level of parking lot data transfer is 100 %.
Key words: Internet of things / IoT cloud / Parking lots / Smart parking system
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.