Open Access

ARTICLE

Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

Ayman Khallel Al-Ani^1,*, Shams Ul Arfeen Laghari², Hariprasath Manoharan³, Shitharth Selvarajan⁴, Mueen Uddin⁵

1 Cybersecurity Research Group, Faculty of Computing and Informatics, University Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah, 88400, Malaysia
2 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Gelugor, Penang, 11800, Malaysia
3 Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, 600123, Tamilnadu, India
4 Department of Computer Science & Engineering, Kebri Dehar University, Kebri Dehar, 250, Ethiopia
5 College of Computing and Information Technology, University of Doha for Science and Technology, Doha, 24449, Qatar

* Corresponding Author: Ayman Khallel Al-Ani. Email:

Computers, Materials & Continua 2023, 76(2), 2261-2279. https://doi.org/10.32604/cmc.2023.038534

Received 16 February 2022; Accepted 07 March 2023; Issue published 30 August 2023

Abstract

In this paper, the application of transportation systems in real-time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. Therefore, the combined objective case studies are examined as minimization and maximization criteria, thus increasing the efficiency of the proposed method. Finally, four scenarios are chosen to investigate the projected design’s effectiveness. In all simulated metrics, the proposed approach provides better operational outcomes for an average percentage of 97, thereby reducing the amount of traffic in real-time conditions.

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Keywords

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