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SDIoTPark: A Data Analytics Framework for Smart Parking Using SDN-Based IoT
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DOI：10.1109/JIOT.2024.3373133.png)
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Abstract
En 中文
An enhanced data analytic framework supported by a flexible and manageable underlying network infrastructure is vital to maximize the utilization of IoT technology. IoT technology facilitates innumerable applications involving decision makings in real time. Smart Parking involving IoT network is one of the important component of Smart City framework. Proper management of parking spaces and finding an empty parking slot in real time saves drivers time and also causes less traffic congestion. In this article, a smart parking framework, SDIoTPark, powered by IoT technology, is presented. A sophisticated networking paradigm involving SDN-based IoT networking was proposed that displayed many potential benefits like flexible, reliable, robust and automatic configuration of smart parking system. A suitable lightweight convolutional neural network-based computer vision tool, namely, SDIoTParkNet was designed for the power- and resource-constrained IoT setup for its real-time applicability. The proposed system provides a Web app for the users to view occupancy status in real time. The system displayed universal applicability. The same was experimented in test bed setup and the results indicate improvement in performance both in terms of network management and data analytic paradigm. It displayed high accuracy and less time requirement with respect to existing tools.
Keywords:
Internet of Things
Real-time systems
Vehicles
Data analysis
Intelligent sensors
Computer vision
Computer architecture
Data analytics
lightweight convolutional neural network (CNN)
smart parking
software-defined network
Journal
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7.8W
