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Smart city article details

Title	Clustering Techniques For Smart Cities: An Artificial Intelligence Perspective
ID_Doc	14535
Authors	Tharwat M.; Khattab A.
Year	2021
Published	Lecture Notes in Intelligent Transportation and Infrastructure, Part F1386
DOI	http://dx.doi.org/10.1007/978-3-030-60922-1_6
Abstract	The recent exponential growth of the number of connected devices in smart cities is expected to further increase over the next decade. Smart cities exploit the Internet of Things (IoT) technology to create cyber-physical systems. Smart city evolution through the IoT technology brings a new era of smart healthcare, industrial, automotive, security, precision agriculture and military systems. Such smart cyber-physical systems face many challenges due to their design and operation characteristics as well as their energy, processing, and memory constraints. Another major challenge faced by smart cities is the dynamic changes in the topology due to the mobility of the devices and the damaged devices which frequently change the system characteristics. Furthermore, the scalability of such systems requires robust design techniques to handle the large number of connected devices in a limited area. Clustering is a prominent technique used to solve the scalability problems and provide a robust operational network in highly dynamic environments such as IoT-based smart systems. Recently, artificial intelligence and machine learning have been exploited to revolutionize the way clustering is performed. In this chapter, we present a comprehensive survey of the artificial intelligence-based clustering techniques for cyber-physical systems in smart cities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Artificial intelligence; Clustering; Internet of Things (IoT); Machine learning; Smart city

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