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 >

Sujie Shao, Zhengpu Wang^*, Siya Xu, Shaoyong Guo, Xuesong Qiu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4949-4972, 2025, DOI:10.32604/cmc.2025.065053 - 30 July 2025

Abstract With the exponential growth of mobile terminals and the widespread adoption of Internet of Things (IoT) technologies, an increasing number of devices rely on wireless local area networks (WLAN) for data transmission. To address this demand, deploying more access points (APs) has become an inevitable trend. While this approach enhances network coverage and capacity, it also exacerbates co-channel interference (CCI). The multi-AP cooperation introduced in IEEE 802.11be (Wi-Fi 7) represents a paradigm shift from conventional single-AP architectures, offering a novel solution to CCI through joint resource scheduling across APs. However, designing efficient cooperation mechanisms and… More >

Tareq Hamadneh¹, Belal Batiha², Frank Werner^3,*, Mehrdad Ahmadi Kamarposhti^4,*, Ilhami Colak⁵, El Manaa Barhoumi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1519-1548, 2025, DOI:10.32604/cmes.2025.063571 - 30 May 2025

Abstract Reconfiguration, as well as optimal utilization of distributed generation sources and capacitor banks, are highly effective methods for reducing losses and improving the voltage profile, or in other words, the power quality in the power distribution system. Researchers have considered the use of distributed generation resources in recent years. There are numerous advantages to utilizing these resources, the most significant of which are the reduction of network losses and enhancement of voltage stability. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Intersect Mutation Differential Evolution (IMDE) algorithms are used in this… More >

Sreebha Bhaskaran^*, Supriya Muthuraman

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5037-5070, 2025, DOI:10.32604/cmc.2025.062657 - 19 May 2025

Abstract Efficient resource provisioning, allocation, and computation offloading are critical to realizing low-latency, scalable, and energy-efficient applications in cloud, fog, and edge computing. Despite its importance, integrating Software Defined Networks (SDN) for enhancing resource orchestration, task scheduling, and traffic management remains a relatively underexplored area with significant innovation potential. This paper provides a comprehensive review of existing mechanisms, categorizing resource provisioning approaches into static, dynamic, and user-centric models, while examining applications across domains such as IoT, healthcare, and autonomous systems. The survey highlights challenges such as scalability, interoperability, and security in managing dynamic and heterogeneous infrastructures. More >

Seyha Ros^1,#, Seungwoo Kang^1,#, Taikuong Iv¹, Inseok Song¹, Prohim Tam², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1649-1665, 2025, DOI:10.32604/cmc.2025.062716 - 16 April 2025

Abstract Recently, Network Functions Virtualization (NFV) has become a critical resource for optimizing capability utilization in the 5G/B5G era. NFV decomposes the network resource paradigm, demonstrating the efficient utilization of Network Functions (NFs) to enable configurable service priorities and resource demands. Telecommunications Service Providers (TSPs) face challenges in network utilization, as the vast amounts of data generated by the Internet of Things (IoT) overwhelm existing infrastructures. IoT applications, which generate massive volumes of diverse data and require real-time communication, contribute to bottlenecks and congestion. In this context, Multi-access Edge Computing (MEC) is employed to support resource… More >