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Title	Traffic Localization In Smart Cities Using Internet Of Crowd Sourcing
ID_Doc	58619
Authors	Elnour R.A.M.; Saeed M.M.; Yousif I.; Mokhtar R.A.
Year	2024
Published	1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024
DOI	http://dx.doi.org/10.1109/ICETI63946.2024.10777134
Abstract	This paper aims to examine how IoT localization technologies have impacted on the smart city traffic application including Internet Vehicles (IoV), Internet of Crowed Sourcing (IoCS), Internet of Mobility (IoM), and the Internet of Artificial Intelligence (IoAI). IoV improves car to car and car to infrastructure interfaces by increasing traffic flow and reducing or eliminating risks. IoAI employs big data and machine learning technology to accurately capture traffic information and to timely signal appropriate traffic lights for better and safe navigation of self-driven cars. IoCS extending it to Crowdsourcing infrastructure to real-time data systems to improve traffic control and safety of pedestrians. IoM develops a single unified network of transportation opportunities incorporating both mass transit and personal vehicle modes, as well as micro-mobility solutions. Potential issues like data protection, integration, and system extensibility are also discussed throughout the paper, together with potential advancements in autonomous vehicles and artificial intelligence traffic management. Lastly, the research discusses the prospects of IoT localization for Smart City transportation developments. These systems integrating services like sensors, GPS, RFID and AI based models provide real time navigation, thus resulting in efficient traffic control and enhancing the prospects of developing sustainable urban environments. © 2024 IEEE.
Author Keywords	Internet of Artificial Intelligence (IoAI); Internet of Crowed Sourcing (IoCS); Internet of Mobile (IoM); Internet of Things (IoT); Internet of Vehicle (IoV)); Traffic congestion (TC)

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