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Smart city article details

Title	Analysis Of Digital Twins Implementation In Smart City Using Big Data And Deep Learning
ID_Doc	9143
Authors	Nayak C.K.; Karunakaran S.; Yamunaa P.; Kayalvili S.; Tiwari M.; Unni M.V.
Year	2023
Published	Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023
DOI	http://dx.doi.org/10.1109/ICICCS56967.2023.10142813
Abstract	Digital twins are becoming more relevant for business and academic users due to advances in IoT, AI, and Big Data. Due to global urbanization, pollution, public safety, traffic congestion, and other challenges have arisen. New technologies make cities smarter to keep up with growth. In the Internet of Things (IoT) age, many sensing devices acquire and/or produce a broad range of sensory data over long periods of time for a variety of businesses and applications. The use case determines the device's data stream volume and speed. The efficacy of the analytics process used to analyze these streams of data to learn, predict, and act determines IoT's worth as a business paradigm changer and quality-of-life technology. This study introduces Deep Learning (DL), a family of advanced machine learning techniques, to enhance IoT analytics and teaching. Introducing new results, challenges, and research opportunities. This study may assist academics and newbies comprehend how to use DL to smart cities. Analyzing and summarizing major IoT DL research initiatives. Check out smart IoT devices with DL embedded into their AI. Ultimately, the study will identify issues and suggest additional research. Each chapter concludes with experimental findings and the newest literature review. © 2023 IEEE.
Author Keywords	Big Data Analysis; Deep Learning; Digital Twin; Internet of Things; Smart City

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