Venkata Satya Suresh kumar Kondeti¹, Raghavendra Kulkarni¹, Binu Sudhakaran Pillai², Surendran Rajendran^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4973-4995, 2025, DOI:10.32604/cmc.2025.066428 - 30 July 2025

Abstract Next-generation 6G networks seek to provide ultra-reliable and low-latency communications, necessitating network designs that are intelligent and adaptable. Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures. Nonetheless, sustaining elevated Quality of Service (QoS) in dynamic, resource-limited systems poses significant hurdles. This study introduces an innovative packet-based proactive end-to-end (ETE) resource management system that facilitates network slicing with improved resilience and proactivity. To get around the drawbacks of conventional reactive systems, we develop a cost-efficient slice provisioning architecture that takes into account limits on radio, processing, and… More >

Yu-Hsien Lin^*, Po-Cheng Chuang, Joyce Yi-Tzu Huang

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4907-4948, 2025, DOI:10.32604/cmc.2025.065995 - 30 July 2025

Abstract This study proposes an automatic control system for Autonomous Underwater Vehicle (AUV) docking, utilizing a digital twin (DT) environment based on the HoloOcean platform, which integrates six-degree-of-freedom (6-DOF) motion equations and hydrodynamic coefficients to create a realistic simulation. Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements, deep reinforcement learning (DRL) offers a promising alternative. In the positioning stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed for synchronized depth and heading control, which offers stable training, reduced overestimation… More >

Qing Zhu¹, Denghui Guo¹, Rui Ruan¹, Zhidong Chai¹, Chaoqun Wang², Zhiwen Guan^2,*

Energy Engineering, Vol.122, No.8, pp. 3133-3154, 2025, DOI:10.32604/ee.2025.063165 - 24 July 2025

Abstract This study presents an emergency control method for sub-synchronous oscillations in wind power grid-connected systems based on transfer learning, addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios. By combining deep reinforcement learning with a transfer learning framework, cross-scenario knowledge transfer is achieved, significantly enhancing the adaptability of the control strategy. First, a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning. A reward evaluation system based on the active power oscillation pattern of the system is… More >

Wenlong Ke^1,2,*, Zilong Li¹, Peiyu Chen¹, Benfeng Chen¹, Jinglin Lv¹, Qiang Wang², Ziyi Jia², Shigen Shen¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3305-3319, 2025, DOI:10.32604/cmc.2025.065665 - 03 July 2025

Abstract Zero Trust Network (ZTN) enhances network security through strict authentication and access control. However, in the ZTN, optimizing flow control to improve the quality of service is still facing challenges. Software Defined Network (SDN) provides solutions through centralized control and dynamic resource allocation, but the existing scheduling methods based on Deep Reinforcement Learning (DRL) are insufficient in terms of convergence speed and dynamic optimization capability. To solve these problems, this paper proposes DRL-AMIR, which is an efficient flow scheduling method for software defined ZTN. This method constructs a flow scheduling optimization model that comprehensively considers… More >

Qian Wang¹, Ya Zhou^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3835-3856, 2025, DOI:10.32604/cmc.2025.065465 - 03 July 2025

Abstract Edge computing has transformed smart grids by lowering latency, reducing network congestion, and enabling real-time decision-making. Nevertheless, devising an optimal task-offloading strategy remains challenging, as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions. We cast the offloading problem as a Markov Decision Process (MDP) and solve it with Deep Reinforcement Learning (DRL). Specifically, we present a three-tier architecture—end devices, edge nodes, and a cloud server—and enhance Proximal Policy Optimization (PPO) to learn adaptive, energy-aware policies. A Convolutional Neural Network (CNN) extracts high-level features from system states, enabling More >

Chenxi Lyu¹, Chen Dong¹, Qiancheng Xiong¹, Yuzhong Chen¹, Qian Weng^1,*, Zhenyi Chen²

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3371-3391, 2025, DOI:10.32604/cmc.2025.065153 - 03 July 2025

Abstract The rapid advancement of Industry 4.0 has revolutionized manufacturing, shifting production from centralized control to decentralized, intelligent systems. Smart factories are now expected to achieve high adaptability and resource efficiency, particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands. To address the challenges of dynamic task allocation, uncertainty, and real-time decision-making, this paper proposes Pathfinder, a deep reinforcement learning-based scheduling framework. Pathfinder models scheduling data through three key matrices: execution time (the time required for a job to complete), completion time (the actual time at which a job is finished),… More >

Chao Li^1,3,#, Quanzhi Feng^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3605-3621, 2025, DOI:10.32604/cmc.2025.063703 - 03 July 2025

Abstract The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts… More >

Shaowei He, Wenchao Cui^*, Gang Li, Hairun Xu, Xiang Chen, Yu Tai

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 861-886, 2025, DOI:10.32604/cmc.2025.063979 - 09 June 2025

Abstract The Virtual Power Plant (VPP), as an innovative power management architecture, achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources. However, due to significant differences in operational costs and flexibility of various types of generation resources, as well as the volatility and uncertainty of renewable energy sources (such as wind and solar power) and the complex variability of load demand, the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed. To solve this, this paper proposes an intelligent scheduling method for virtual power… More >

Peiying Zhang^1,2,*, Yihong Yu^1,2, Jing Liu³, Chong Lv^1,2, Lizhuang Tan^4,5, Yulin Zhang^6,7,8

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4393-4409, 2025, DOI:10.32604/cmc.2025.064654 - 19 May 2025

Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… 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 >