Meshari D. Alanazi¹, Gehan Elsayed^2,*, Turki M. Alanazi³, Anis Sahbani⁴, Amr Yousef^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2277-2309, 2025, DOI:10.32604/cmes.2025.070726 - 26 November 2025

Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >

Fan Chen^*, Xian Bao, Jianlin Liu, Man Wang, Qiang Zhang

Journal on Artificial Intelligence, Vol.7, pp. 365-380, 2025, DOI:10.32604/jai.2025.069903 - 06 October 2025

Abstract The Thyristor-Controlled Series Compensator (TCSC) presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow. However, inherent faults within the TCSC may lead to an unintended intensification of transmission congestion in other sections of the system post-installation, resulting in non-coherent phenomena of line blocking. In response to this challenge, this paper introduces a novel two-stage site selection method for TCSC, emphasizing the enhancement of coherence in addressing line-blocking issues. Through rigorous non-coherent verification, this method mitigates the risk of line congestion deterioration due to TCSC faults.… More >

Seongsoo Cho¹, Yeonwoo Lee^2,*, Cheolhee Yoon^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3769-3793, 2025, DOI:10.32604/cmes.2025.066298 - 30 June 2025

Abstract As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions, efficient clustering mechanisms are vital to ensure stable and scalable communication. Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems (ITS). This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering (GOA-VNET) algorithm, an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks (VANETs), leveraging the Grasshopper Optimization Algorithm (GOA) to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems (ITS). The proposed GOA-VNET employs an… More >

Idongesit Sunday Ambrose¹, Sunday Edet Etuk², Okechukwu Ebuka Agbasi^3,*, Ijah Ioryue Silas⁴, Unyime Udoette Saturday⁵, Eyo Edet Orok⁶

Revue Internationale de Géomatique, Vol.34, pp. 335-350, 2025, DOI:10.32604/rig.2025.064552 - 13 June 2025

Abstract Road construction and traffic congestion are increasingly recognized as major contributors to environmental and public health challenges in urban Nigeria, particularly in Rivers State. Despite growing urbanization, a gap remains in localized data on the combined effects of air and noise pollution in hydrocarbon-polluted environments. This study addresses that gap by conducting a preliminary environmental health assessment focused on the Port Harcourt Ring Road project. Air quality and noise levels were monitored in situ at 20 strategically selected locations, with five control points included for baseline comparison. Digital portable meters were used to measure concentrations of… More >

Lingqiu Zeng¹, Peibing Sa¹, Qingwen Han², Lei Ye^2,*, Letian Yang¹, Cheng Zhang¹, Jiqiang Cheng²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2059-2086, 2025, DOI:10.32604/cmc.2025.060392 - 16 April 2025

Abstract Congestion control is an inherent challenge of V2X (Vehicle to Everything) technologies. Due to the use of a broadcasting mechanism, channel congestion becomes severe with the increase in vehicle density. The researchers suggested reducing the frequency of packet dissemination to relieve congestion, which caused a rise in road driving risk. Obviously, high-risk vehicles should be able to send messages timely to alarm surrounding vehicles. Therefore, packet dissemination frequency should be set according to the corresponding vehicle’s risk level, which is hard to evaluate. In this paper, a two-stage fuzzy inference model is constructed to evaluate More >

Jinlin Xu^1,2, Wansu Pan^1,*, Haibo Tan^1,2, Longle Cheng¹, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2709-2726, 2024, DOI:10.32604/cmc.2024.056925 - 18 November 2024

Abstract The traffic within data centers exhibits bursts and unpredictable patterns. This rapid growth in network traffic has two consequences: it surpasses the inherent capacity of the network’s link bandwidth and creates an imbalanced network load. Consequently, persistent overload situations eventually result in network congestion. The Software Defined Network (SDN) technology is employed in data centers as a network architecture to enhance performance. This paper introduces an adaptive congestion control strategy, named DA-DCTCP, for SDN-based Data Centers. It incorporates Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) to establish congestion awareness and an ECN marking model.… More >

Ahmed Alhussen¹, Arshiya S. Ansari^2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1903-1923, 2024, DOI:10.32604/cmc.2024.049260 - 15 May 2024

Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data… More >

Abdulwahab Ali Almazroi^1,*, Nasir Ayub²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3921-3942, 2023, DOI:10.32604/cmc.2023.046602 - 26 December 2023

Abstract Effectively managing complex logistics data is essential for development sustainability and growth, especially in optimizing distribution routes. This article addresses the limitations of current logistics path optimization methods, such as inefficiencies and high operational costs. To overcome these drawbacks, we introduce the Hybrid Firefly-Spotted Hyena Optimization (HFSHO) algorithm, a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm (FO) with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm (SHO). HFSHO aims to improve logistics path optimization and reduce operational costs. The algorithm’s effectiveness is systematically… More >

Ali Alshahrani¹, Ahmad Adel Abu-Shareha^2,*, Qusai Y. Shambour², Basil Al-Kasasbeh¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1679-1698, 2023, DOI:10.32604/cmc.2023.043545 - 29 November 2023

Abstract Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based… More >

Babangida Isyaku^1,2,*, Kamalrulnizam Bin Abu Bakar¹, Wamda Nagmeldin³, Abdelzahir Abdelmaboud⁴, Faisal Saeed^5,6, Fuad A. Ghaleb¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3729-3748, 2023, DOI:10.32604/csse.2023.034509 - 03 April 2023

Abstract Software Defined Networks (SDN) introduced better network management by decoupling control and data plane. However, communication reliability is the desired property in computer networks. The frequency of communication link failure degrades network performance, and service disruptions are likely to occur. Emerging network applications, such as delay-sensitive applications, suffer packet loss with higher Round Trip Time (RTT). Several failure recovery schemes have been proposed to address link failure recovery issues in SDN. However, these schemes have various weaknesses, which may not always guarantee service availability. Communication paths differ in their roles; some paths are critical because… More >