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Analysis of Smart Parking System Techniques: An analysis of the most optimal techniques to develop a smart parking system
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2021 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
Sustainable development
Not refering to any SDG
Abstract [en]

In today’s society, finding available parking can sometimes be a time consuming process. Drivers may gather in the same areas and compete with each other for the sameparking spots, thus making the areas overcrowded. The time consuming process offinding available parking spots has a negative impact on the environment as morefuel is consumed, which increases air pollution. In order to tackle these problems, asmart parking system can be developed. Such a system is able to detect when a parking spot is empty or occupied and relay this information to drivers, to help them findavailable parking spots. In order for a smart parking system to achieve wide-spreadacceptance, it must be of low cost, offer high accuracy, high mounting capability &high scalability. Because of this, the following question arises: What are the besttechniques to use for a smart parking system in order to achieve low cost, high accuracy, high mounting capability & high scalability? This project attempts to answerthis question by clearly defining the aforementioned attributes and by performing astructured search of available techniques. A comparison is then made in order to findthe best solutions for each attribute, and for all attributes combined. The results showthat Ultrasonic sensors or AI Cameras are best in terms of cost, and Magnetometersare best in terms of accuracy. In terms of mounting capability and scalability, sixdifferent sensors are found to be the best option for both attributes.

Place, publisher, year, edition, pages
2021. , p. 72
Keywords [en]
smart parking systems, sensor nodes, cost analysis, accuracy analysis, mounting capability analysis, scalability analysis, wireless communication technologies, vehicle detection technologies, microcontrollers, batteries
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:lnu:diva-106237OAI: oai:DiVA.org:lnu-106237DiVA, id: diva2:1587308
Subject / course
Technology
Educational program
Software Engineering Programme, 180 credits
Presentation
2021-06-03, Zoom, online, Zoom, online, Zoom, online, 11:35 (English)
Supervisors
Examiners
Available from: 2021-08-24 Created: 2021-08-24 Last updated: 2021-08-24Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf