Stellar Choi¹, HeeAe Ko², KyungRok Bae³, HyunSook Lee², HaeJong Joo⁴, Woong Choi^5,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 335-355, 2025, DOI:10.32604/cmc.2025.061243 - 26 March 2025

Abstract Previous research utilizing Cartoon Generative Adversarial Network (CartoonGAN) has encountered limitations in managing intricate outlines and accurately representing lighting effects, particularly in complex scenes requiring detailed shading and contrast. This paper presents a novel Enhanced Pixel Integration (EPI) technique designed to improve the visual quality of images generated by CartoonGAN. Rather than modifying the core model, the EPI approach employs post-processing adjustments that enhance images without significant computational overhead. In this method, images produced by CartoonGAN are converted from Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) format, allowing for precise adjustments in hue, saturation, and brightness, thereby… More >

Ferzat Anka¹, Ghanshyam G. Tejani^2,3,*, Sunil Kumar Sharma⁴, Mohammed Baljon⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2691-2724, 2025, DOI:10.32604/cmes.2025.061522 - 03 March 2025

Abstract Due to the intense data flow in expanding Internet of Things (IoT) applications, a heavy processing cost and workload on the fog-cloud side become inevitable. One of the most critical challenges is optimal task scheduling. Since this is an NP-hard problem type, a metaheuristic approach can be a good option. This study introduces a novel enhancement to the Artificial Rabbits Optimization (ARO) algorithm by integrating Chaotic maps and Levy flight strategies (CLARO). This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed. It is designed for task scheduling… More >

Sonia Khan¹, Naqash Younas², Musaed Alhussein³, Wahib Jamal Khan², Muhammad Shahid Anwar^4,*, Khursheed Aurangzeb³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2641-2660, 2025, DOI:10.32604/cmes.2025.060973 - 03 March 2025

Abstract Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power More >

Adil Yousif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025

Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

Usman Mahmood Malik¹, Muhammad Awais Javed², Ahmad Naseem Alvi², Mohammed Alkhathami^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2255-2274, 2025, DOI:10.32604/cmc.2025.061254 - 17 February 2025

Abstract Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing, and data storage services which are required for several 6G applications. Artificial Intelligence (AI) algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability. In this paper, the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers (POMH) in which larger tasks are divided into smaller subtasks and processed in parallel, hence expediting task completion. However, using POMH presents challenges… More >

Ibrar Afzal¹, Noor ul Amin^1,*, Zulfiqar Ahmad^1,*, Abdulmohsen Algarni²

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1377-1399, 2025, DOI:10.32604/cmc.2024.057755 - 03 January 2025

Abstract The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related… More >

Abduladheem Fadhil Khudhur^*, Ayça Kurnaz Türkben, Sefer Kurnaz

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3805-3824, 2024, DOI:10.32604/cmc.2024.056941 - 19 December 2024

Abstract The fast growth in Internet-of-Vehicles (IoV) applications is rendering energy efficiency management of vehicular networks a highly important challenge. Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing, this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios. The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices. The proposed LEAF model enables researchers to perform simulations of… More >

Ahamad B. Alkodre¹, Nour Mahmoud Bahbouh², Sandra Sendra³, Adnan Ahmed Abi Sen^4,*, Yazed Alsaawy¹, Saad Said Alqahtany¹, Abdallah Namoun¹, Hani Almoamari¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2727-2751, 2024, DOI:10.32604/cmc.2024.053706 - 18 November 2024

Abstract In Saudi Arabia, drones are increasingly used in different sensitive domains like military, health, and agriculture to name a few. Typically, drone cameras capture aerial images of objects and convert them into crucial data, alongside collecting data from distributed sensors supplemented by location data. The interception of the data sent from the drone to the station can lead to substantial threats. To address this issue, highly confidential protection methods must be employed. This paper introduces a novel steganography approach called the Shuffling Steganography Approach (SSA). SSA encompasses five fundamental stages and three proposed algorithms, designed… More >

Naeem Raza¹, Muhammad Asif Habib¹, Mudassar Ahmad¹, Qaisar Abbas^2,*, Mutlaq B. Aldajani², Muhammad Ahsan Latif³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 911-931, 2024, DOI:10.32604/cmc.2024.055049 - 15 October 2024

Abstract Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle… More >

Hawazen Alzahrani¹, Tarek Sheltami¹, Abdulaziz Barnawi², Muhammad Imam^2,*, Ansar Yaser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4703-4728, 2024, DOI:10.32604/cmc.2024.054203 - 12 September 2024

Abstract The Internet of Things (IoT) links various devices to digital services and significantly improves the quality of our lives. However, as IoT connectivity is growing rapidly, so do the risks of network vulnerabilities and threats. Many interesting Intrusion Detection Systems (IDSs) are presented based on machine learning (ML) techniques to overcome this problem. Given the resource limitations of fog computing environments, a lightweight IDS is essential. This paper introduces a hybrid deep learning (DL) method that combines convolutional neural networks (CNN) and long short-term memory (LSTM) to build an energy-aware, anomaly-based IDS. We test this… More >