Development of an IoT-Based Parking Space Management System Design

Wang, Ange

doi:10.47738/ijaim.v3i2.54

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…To this end, Wang et al [17] select TechIO technology to enable wireless communiqué lot, examine the benefits of TechIO technology and the requirements of a parking management system, and construct a simulation based on the system design to conduct relevant tests on the system's components. From the results of the tests, it is clear that the system's use of TechIO technology results in its strong adaptability, removes the need for expensive and time-consuming wiring in the parking lot but also greatly simplifies the deployment of parking lot nodes.…”

Section: Related Workmentioning

confidence: 99%

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

et al. 2024

Int. Res. J. multidiscip. Technovation

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Uneven illumination has a significant impact on vision-based automatic parking systems, making it impossible to conduct a correct assessment of parking places in the presence of complicated picture data. In to address this issue, this work provides a deep learning-based system for visual recognition of parking spaces and picture processing. Artificial intelligence (AI) approaches can be used to identify a less expensive and easier-to-implement solution to the parking spot identification challenge, especially since the discipline of deep learning is reshaping the world. Using deep learning techniques, this study offers a dynamic, straightforward, and cost-effective algorithm for the detection of parking spots. In order to determine which parking spots are available and which are occupied, this method employs a Masked Region Based Convolutional Neural Network (MR-CNN) and the intersection over union approach. Cars in the training dataset were spaced more apart than those actually seen, which increased the accuracy of the identification between cars and parking spots. The AOA mechanism enhances the model's ability to focus on relevant regions within an image, improving accuracy in detecting parking spaces. This leads to precise identification of parking slots, reducing false positives and negatives. The sequence and quantity of parking spots, as well as the capacity to predict empty spots, were tested in a case study and found to be accurate. In the experimental results as the AOA based MR-CNN model stretched the accuracy as 98.50 and the recall value as 40.59 then the precision as 96.34 F1-measure as 57.95 correspondingly.

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Section: Related Workmentioning

confidence: 99%

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

et al. 2024

Int. Res. J. multidiscip. Technovation

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“…Smart buildings start from the needs of people and collect a small amount of data on specific aspects. Building digitization is to collect all kinds of data as much as possible [3,4].…”

Section: The Difference Between Building Data and Building Intelligencementioning

confidence: 99%

Research on Data Fusion Algorithm of Intelligent Building Based on Internet of Things Technology

Zhuang

2023

IJIIS Int. J. Informatics Inf. Syst.

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In an era of sustained economic growth and rising living standards, the demand for housing continues to increase significantly, driving the development of the construction industry and the inevitable transformation of architectural digitization as a prevailing trend. This circumstance has generated limitless economic opportunities across various construction-related sectors, including building materials, technology, and related services. Simultaneously, the evolving Internet of Things (IoT) technology has paved the way for the digitization of buildings, revolutionizing the entire process of architectural design, construction, and management. Architectural digitization enables real-time monitoring, in-depth data analysis, and the optimization of building efficiencies, encompassing energy management, temperature control, and lighting systems. In the current landscape, architectural digitization has given rise to smart buildings that can effectively control nearly all aspects, from temperature and lighting to security systems and comfort. Furthermore, IoT technology has facilitated addressing sustainability challenges by enabling more efficient and sustainable energy management-an essential aspect of the journey toward environmentally friendly buildings. Nevertheless, on the path to fully interconnected digital buildings, there are challenges that must be addressed, such as data security and user privacy, both of which require special attention. Looking forward, research directions include the development of more sophisticated digital integration algorithms, enhanced data security, and the integration of artificial intelligence into building management. The future of architectural digitization holds great promise, with the potential to create more efficient, secure, and environmentally friendly buildings. To achieve these goals, it is essential to strengthen cross-sector collaboration, receive government support, and emphasize research and innovation in the field of architectural digitization.

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AI-Driven Traffic Optimization in 5G and Beyond: Challenges, Strategies, Solutions, and Prospects

Agbon,

Muhammad,

Alabi

et al. 2024

Lecture Notes in Electrical Engineering

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Development of an IoT-Based Parking Space Management System Design

Cited by 7 publications

References 16 publications

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

Research on Data Fusion Algorithm of Intelligent Building Based on Internet of Things Technology

AI-Driven Traffic Optimization in 5G and Beyond: Challenges, Strategies, Solutions, and Prospects

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