Zeeshan Ali Haider¹, Inam Ullah^2,*, Ahmad Abu Shareha³, Rashid Nasimov⁴, Sufyan Ali Memon^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.071042 - 10 November 2025

Abstract The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network.… More >

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Nujud Aloshban^*, Abeer A.K. Alharbi

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4795-4826, 2025, DOI:10.32604/cmc.2025.067523 - 23 October 2025

Abstract The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent, real-time traffic forecasting solutions that preserve data privacy. This paper introduces NeuroCivitas, a federated deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial pattern recognition and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. Designed to meet the adaptive intelligence requirements of cognitive cities, NeuroCivitas leverages Federated Averaging (FedAvg) to ensure privacy-preserving training while significantly reducing communication overhead—by 98.7% compared to centralized models. The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records… More >

Syed Zain Ul Abideen¹, Mian Muhammad Kamal^2,*, Eaman Alharbi³, Ashfaq Ahmad Malik⁴, Wadee Alhalabi⁵, Muhammad Shahid Anwar^6,*, Liaqat Ali⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2191-2210, 2025, DOI:10.32604/cmes.2025.061744 - 03 March 2025

Abstract The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet of Things (IoT) applications, particularly in terms of ultra-reliable, secure, and energy-efficient communication. This study explores the integration of Reconfigurable Intelligent Surfaces (RIS) into IoT networks to enhance communication performance. Unlike traditional passive reflector-based approaches, RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes, addressing critical IoT challenges such as energy efficiency, limited communication range, and double-fading effects in backscatter communication. We propose a novel computational framework that combines… More >

Borja Bordel Sánchez^1,*, Ramón Alcarria², Tomás Robles¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349 - 20 August 2024

Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

Qusay M. Salih^1,2,*, Md. Arafatur Rahman³, Ahmad Firdaus¹, Mohammed Rajih Jassim⁴, Hasan Kahtan⁵, Jasni Mohamad Zain⁶, Ahmed Hussein Ali⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2165-2206, 2024, DOI:10.32604/cmes.2024.028132 - 08 July 2024

Abstract The race to develop the next generation of wireless networks, known as Sixth Generation (6G) wireless, which will be operational in 2030, has already begun. To realize its full potential over the next decade, 6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones. However, the studies about 6G are mainly limited and scattered, whereas no bibliometric study covers the 6G field. Thus, this study aims to review, examine, and summarize existing studies and research activities in 6G. This study has examined the Scopus database through a bibliometric analysis of more than More > Graphic Abstract

Mrim M. Alnfiai^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5723-5740, 2023, DOI:10.32604/cmc.2023.033408 - 28 December 2022

Abstract In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent… More >

Abdelhamied A. Ateya¹, Amel Ali Alhussan^2,*, Hanaa A. Abdallah³, Mona A. Al duailij², Abdukodir Khakimov⁴, Ammar Muthanna⁵

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031841 - 03 November 2022

Abstract Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, More >

Anwer Mustafa Hilal^1,*, Jaber S. Alzahrani², Ibrahim Abunadi³, Nadhem Nemri⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Ishfaq Yaseen¹, Abu Sarwar Zamani¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 333-348, 2022, DOI:10.32604/cmc.2022.024794 - 24 February 2022

Abstract In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims… More >

Mesfer Al Duhayyim¹, Marwa Obayya², Fahd N. Al-Wesabi^3,4, Anwer Mustafa Hilal^5,*, Mohammed Rizwanullah⁵, Majdy M. Eltahir⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 825-842, 2022, DOI:10.32604/cmc.2022.021490 - 03 November 2021

Abstract With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel… More >