Open Access

ARTICLE

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

Abida Sharif¹, Imran Sharif¹, Muhammad Asim Saleem², Muhammad Attique Khan³, Majed Alhaisoni⁴, Marriam Nawaz^5,6, Abdullah Alqahtani⁷, Ye Jin Kim⁸, Byoungchol Chang^9,*

1 Department of Computer Science, COMSATS University Islamabad, Pakistan
2 Faculty of Computing, Riphah International University, Faisalabad, Pakistan
3 Department of Computer Science, HITEC University, Taxila, Pakistan
4 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
5 Department of Computer Science, UET Taxila, Taxila, Pakistan
6 Department of Software Engineering, UET Taxila, Taxila, Pakistan
7 Software Engineering Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj, 11942, KSA
8 Department of Computer Science, Hanyang University, Seoul, 04763, Korea
9 Center for Computational Social Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Byoungchol Chang. Email:

Computers, Materials & Continua 2023, 75(3), 5379-5393. https://doi.org/10.32604/cmc.2023.034413

Received 16 July 2022; Accepted 02 February 2023; Issue published 29 April 2023

Abstract

The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path planning optimization problem as an Integer Linear Program (ILP). This integrates the future estimation metric to predict the future arrivals of the vehicles, searching the optimal routes. Considering the mobile nature of IOV, fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path. The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path. Thus, this work strongly supports its use in applications having stringent Quality of Service (QoS) requirements for the vehicles.

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Keywords

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