Boda Zhang^1,*, Fuhua Luo¹, Yunhao Yu¹, Chameiling Di¹, Ruibin Wen¹, Fei Chen²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069323 - 27 January 2026

Abstract The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems (CPDS). While enabling advanced functionalities, the tight interdependence between cyber and physical layers introduces significant security challenges and amplifies operational risks. To address these critical issues, this paper proposes a comprehensive risk assessment framework that explicitly incorporates the physical dependence of information systems. A Bayesian attack graph is employed to quantitatively evaluate the likelihood of successful cyber attacks. By analyzing the critical scenario of fault current path misjudgment, we define novel system-level and node-level risk coupling indices to precisely measure the… More >

Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073818 - 12 January 2026

Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

Muhammad Ejaz¹, Muhammad Asim^2,*, Mudasir Ahmad Wani^2,3, Kashish Ara Shakil^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072464 - 12 January 2026

Abstract The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. More >

Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

Zeyu Chen¹, Jian Sun^2,*, Zhengda Huan¹, Ziyi Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.071182 - 09 December 2025

Abstract To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods More >

Malvinder Singh Bali¹, Weiwei Jiang^2,*, Saurav Verma³, Kanwalpreet Kour⁴, Ashwini Rao³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070866 - 09 December 2025

Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

Rekha Phadke¹, Abdul Lateef Haroon Phulara Shaik², Dayanidhi Mohapatra³, Doaa Sami Khafaga^4,*, Eman Abdullah Aldakheel⁴, N. Sathyanarayana⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.067564 - 09 December 2025

Abstract Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process… More >

Siraj Un Muneer¹, Ihsan Ullah¹, Zeshan Iqbal^2,*, Rajermani Thinakaran³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4959-4975, 2025, DOI:10.32604/cmc.2025.068566 - 23 October 2025

Abstract The complexity of cloud environments challenges secure resource management, especially for intrusion detection systems (IDS). Existing strategies struggle to balance efficiency, cost fairness, and threat resilience. This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm (GA) with a “double auction” method. This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework. It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders. The GA functions as an intelligent search mechanism that identifies optimal combinations of bids More >

Puneet Sharma¹, Dhirendra Prasad Yadav¹, Bhisham Sharma^2,*, Surbhi B. Khan^3,4,*, Ahlam Almusharraf ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2179-2193, 2025, DOI:10.32604/cmc.2025.063190 - 29 August 2025

Abstract The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems. This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network (QT-DNN) with Binary Bird Swarm Optimization (BBSO) to enhance resource allocation while preserving Quality of Service (QoS). In contrast to conventional approaches, the QT-DNN accurately predicts task-resource mappings using tensor-based task representation, significantly minimizing computing overhead. The BBSO allocates resources dynamically, optimizing energy efficiency and task distribution. Experimental results from extensive simulations indicate the efficacy of the suggested strategy; the… More >

Zhengda Huan¹, Jian Sun^2,*, Zeyu Chen¹, Ziyi Zhang¹, Xiao Sun¹, Zenghui Xiao¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5427-5443, 2025, DOI:10.32604/cmc.2025.065860 - 30 July 2025

Abstract With the advancement of Vehicle-to-Everything (V2X) technology, efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance. Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions. To address these challenges, this study presents an innovative framework that combines Graph Neural Networks (GNNs) with a Double Deep Q-Network (DDQN), utilizing dynamic graph structures and reinforcement learning. An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology, thereby improving decision accuracy and… More >