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Analysis of Smart Parking System Techniques: An analysis of the most optimal techniques to develop a smart parking system
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
2021 (engelsk)Independent thesis Basic level (university diploma), 10 poäng / 15 hpOppgave
Hållbar utveckling
Berör inget 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.

sted, utgiver, år, opplag, sider
2021. , s. 72
Emneord [en]
smart parking systems, sensor nodes, cost analysis, accuracy analysis, mounting capability analysis, scalability analysis, wireless communication technologies, vehicle detection technologies, microcontrollers, batteries
HSV kategori
Identifikatorer
URN: urn:nbn:se:lnu:diva-106237OAI: oai:DiVA.org:lnu-106237DiVA, id: diva2:1587308
Fag / kurs
Technology
Utdanningsprogram
Software Engineering Programme, 180 credits
Presentation
2021-06-03, Zoom, online, Zoom, online, Zoom, online, 11:35 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2021-08-24 Laget: 2021-08-24 Sist oppdatert: 2021-08-24bibliografisk kontrollert

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