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 >

Qinghai Ou¹, Min Yang¹, Jingcai Kong¹, Yang Yang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2429-2447, 2025, DOI:10.32604/cmc.2025.062067 - 16 April 2025

Abstract The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors (SPSs) in smart grid networks. In such cases, a space-air-ground integrated network serves as an effective emergency solution. This study addresses the challenge of optimizing the energy efficiency of data transmission from SPSs to low Earth orbit (LEO) satellites through unmanned aerial vehicles (UAVs), considering both effective capacity and fronthaul link capacity constraints. Due to the non-convex nature of the problem, the objective function is reformulated, and a delay-aware energy-efficient power allocation and UAV trajectory design (DEPATD)… More >

Abdullah M. Alqahtani^1,*, Kamran Ahmad Awan², Abdulaziz Almaleh³, Osama Aletri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3155-3179, 2025, DOI:10.32604/cmes.2025.061472 - 03 March 2025

Abstract Efficient resource management within Internet of Things (IoT) environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities. This study introduces a neural network-based model that uses Long-Short-Term Memory (LSTM) to optimize resource allocation under dynamically changing conditions. Designed to monitor the workload on individual IoT nodes, the model incorporates long-term data dependencies, enabling adaptive resource distribution in real time. The training process utilizes Min-Max normalization and grid search for hyperparameter tuning, ensuring high resource utilization and consistent performance. The simulation results demonstrate the effectiveness of the proposed method, More >

Jeta Dobruna^1,2, Zana Limani Fazliu^2,*, Iztok Humar¹, Mojca Volk¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2211-2245, 2025, DOI:10.32604/cmes.2025.059541 - 03 March 2025

Abstract Resource allocation remains a challenging issue in communication networks, and its complexity is continuously increasing with the densification of the networks. With the evolution of new wireless technologies such as Fifth Generation (5G) and Sixth Generation (6G) mobile networks, the service level requirements have become stricter and more heterogeneous depending on the use case. In this paper, we review a large body of literature on various resource allocation schemes that are used in particular in mobile wireless communication networks and compare the proposed schemes in terms of performance indicators as well as techniques used. Our… More >