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Title	Enhanced Traffic Congestion Management With Fog Computing - A Simulation-Based Investigation Using Ifog-Simulator
ID_Doc	23687
Authors	Elsayed A.; Mohamed K.; Harb H.
Year	2024
Published	International Journal of Computer Networks and Communications, 16, 3
DOI	http://dx.doi.org/10.5121/ijcnc.2024.16303
Abstract	Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while still allowing communication with the cloud. Many applications rely on fog computing, including traffic management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog computing and tested in a crowdedCairo city. The results obtained indicate that the execution time of the simulation is 4, 538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which are essential for evaluating the performance of the ITCMS. Our system model is also compared with other models to assess its performance. A comparison is made using two parameters, namely throughput and the total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency. © (2024), (Academy and Industry Research Collaboration Center (AIRCC)). All rights reserved.
Author Keywords	Cloud Computing; Fog Computing; iFog-Simulator; Traffic Congestion; Traffic Index; Traffic Sensing Systems

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