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Smart city article details

Title	An Iot-Platform-Based Deep Learning System For Human Behavior Recognition In Smart City Monitoring Using The Berkeley Mhad Datasets
ID_Doc	8761
Authors	Khalifa O.O.; Roubleh A.; Esgiar A.; Abdelhaq M.; Alsaqour R.; Abdalla A.; Ali E.S.; Saeed R.
Year	2022
Published	Systems, 10, 5
DOI	http://dx.doi.org/10.3390/systems10050177
Abstract	Internet of Things (IoT) technology has been rapidly developing and has been well utilized in the field of smart city monitoring. The IoT offers new opportunities for cities to use data remotely for the monitoring, smart management, and control of device mechanisms that enable the processing of large volumes of data in real time. The IoT supports the connection of instruments with intelligible features in smart cities. However, there are some challenges due to the ongoing development of these applications. Therefore, there is an urgent need for more research from academia and industry to obtain citizen satisfaction, and efficient architecture, protocols, security, and services are required to fulfill these needs. In this paper, the key aspects of an IoT infrastructure for smart cities were analyzed. We focused on citizen behavior recognition using convolution neural networks (CNNs). A new model was built on understanding human behavior by using the berkeley multimodal human action (MHAD) Datasets. A video surveillance system using CNNs was implemented. The proposed model’s simulation results achieved 98% accuracy for the citizen behavior recognition system. © 2022 by the authors.
Author Keywords	artificial intelligence; Berkeley MHAD Datasets; deep learning; Internet of Things; neural networks; smart cities; video surveillance

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