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Title	Sdiotpark: A Data Analytics Framework For Smart Parking Using Sdn-Based Iot
ID_Doc	47476
Authors	Marshoodulla S.Z.; Saha G.
Year	2024
Published	IEEE Internet of Things Journal, 11, 11
DOI	http://dx.doi.org/10.1109/JIOT.2024.3373133
Abstract	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. © 2014 IEEE.
Author Keywords	Data analytics; Internet of Things; lightweight convolutional neural network (CNN); smart parking; software-defined network

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