Daniel Šolc^1,*, René Ivančák¹, Juraj Gazda¹, Eva Chovancová¹, Eugen Šlapák¹, Gabriel Bugár²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088224 - 15 September 2026

Abstract Autonomous mobile robots increasingly rely on three tightly coupled capabilities: low-latency wireless connectivity, edge-side compute acceleration, and simulation-based pre-training of perception and control models. Each has been studied extensively in isolation, but their joint deployment on a single platform remains rare. This paper presents an integrated framework combining a private 5G Stand-Alone (5G SA) access network, a Multi-Access Edge Computing (MEC) layer with adaptive offloading, and a digital-twin training pipeline in NVIDIA Omniverse Isaac Sim. It is realised on the Yahboom ROSMASTER R2 with an NVIDIA Jetson Orin NX, a Quectel RM530N-GL 5G modem in… More >

Zhihao Liu^1,2, Yi Zhang³, Wei Zhang^1,2, Jian Wang⁴, Huiling Shi^1,2, Xiaolong Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088018 - 15 September 2026

Abstract Mobile edge services require deterministic communication across Time-Sensitive Networking (TSN) and 5G access, where the standardized integration architecture exposes the 5G System (5GS) to the TSN controller as a logical bridge. We study dynamic admission of time-triggered (TT) flows using reported 5GS bridge delay and TSN-to-5GS Quality of Service (QoS) mapping in route selection and Gate Control List (GCL) scheduling. Arrivals and departures can split available transmission time into noncontiguous windows. Online insertion preserves admitted schedules but may reduce subsequent schedulability, whereas full recomputation can restore schedulability but changes many routes and GCL entries, complicating… More >

Hui Zhang¹, Mangang Xie^1,*, Baozhen An², Jing Wei¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086401 - 15 September 2026

Abstract Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power More >

Vikas Tyagi^1,*, Mrinmoy Kayal¹, Arvind Prasad^2,*, Gauhar Ali³, Sajid Shah³, Muhammad Asim³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085763 - 15 September 2026

Abstract Healthcare systems based on the Internet of Things (IoT) are widely used in patient monitoring, telemedicine, emergency care, and hospital-at-home services. However, existing IoT healthcare networks still face major challenges related to security, trust management, network control, and real-time emergency data handling. Centralized trust mechanisms and repeated cloud-based verification may increase delay and reduce reliability in critical healthcare scenarios. Moreover, suspicious medical devices must be quickly isolated, while sensitive patient data and emergency traffic must be protected and prioritized. To address these issues, this work proposes a blockchain-enabled, software-defined networking (SDN)-based edge computing framework for… More >

Jaekyung Lee^1,2, Byungsung Ko², Jiwon Lee², Jaeheon Park², Taewon Kim², Seoktae Kim², Wonhee Kim^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085203 - 15 September 2026

Abstract This study proposes the Artificial Intelligence-integrated Inspection Ecosystem (AIIE) as an autonomous condition monitoring platform to enable Prognostics and Health Management (PHM) for underground infrastructure facilities at the Korea Electric Power Corporation (KEPCO) power Information and Communication Technology (ICT) center. To address the environmental dependency of conventional systems, which necessitate extensive control logic redesigns upon changes in target facilities or environments, a three-layer abstraction architecture separating directive, orchestration, and execution roles is established, integrating a quadrupedal robot with heterogeneous sensors into a unified control structure. To overcome the limitation of relying on one general-purpose model… More >

Yixuan Chen, Yufeng Qin^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084385 - 15 September 2026

Abstract Lightweight real-time image denoising is crucial for resource-constrained edge devices, yet existing compact convolutional neural networks (CNNs) often lose high-frequency details due to limited capacity and the absence of explicit frequency-domain supervision. This paper proposes a 0.18M-parameter dual-branch denoising network driven by a novel Frequency Domain Perception Loss (FDPL). The architecture decouples noise removal and detail recovery via a low-frequency branch composed of four Residual-in-Residual Dense Blocks (RRDB) and a high-frequency branch with two Residual Channel Attention Blocks (RCAB). The composite loss combines brightness-aware Mean Square Error (MSE), Visual Geometry Group 19-layer (VGG19) perceptual loss,… More >

Fei Bu¹, Zheng Wang^2,3,*, Yong Pan⁴, Zhaomin Wu¹, Yuchen Liang¹, Zhongshan Zhu⁴, Tengfei Tu⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083956 - 15 September 2026

Abstract Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge,… More >

Pingan Peng^1,2, Qi Zhang^1,*, Ya Liu¹, Yue Han¹, Zhida Jiang¹, Linli Chen¹, Xuefeng Huo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083624 - 28 August 2026

Abstract Unfilled goafs located beneath urban areas pose a significant threat to surface safety, and microseismic monitoring is an important tool for capturing rock-mass microfractures and supporting early warning. However, under field conditions, existing sequential models face challenges such as high computational complexity and deep feature degradation when deployed on edge devices, primarily due to the non-stationary characteristics of microseismic signals and the presence of complex ambient background noise. To address these issues, this paper proposes the Hierarchical Network with Manifold Hybrid Connection (H-NET-mHC), a lightweight manifold-aware state space model designed for edge computing. The model… More >

Zheng Yao¹, Jie Liu¹, Changjun Deng^2,3,*, Wang Lin^2,3

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084892 - 13 August 2026

Abstract Mobile edge computing (MEC) is an effective paradigm for supporting latency-sensitive and computation-intensive intelligent applications. However, in dynamic mobile-edge network scenarios, mobile terminals experience time-varying wireless links due to mobility. Tasks may also arrive unpredictably, while multiple terminals compete for limited edge resources. As a result, MEC systems may suffer from service congestion and unbalanced resource utilization, which increases end-to-end latency and energy consumption. This paper investigates cooperative task offloading in dynamic MEC networks. The considered system comprises one macro base station and multiple small base stations equipped with edge-computing resources. In each time slot,… More >

Nourah Fahad Janbi^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084270 - 13 August 2026

Abstract Automatic pain assessment from speech is an emerging approach for non-invasive and objective healthcare monitoring. However, many existing methods are evaluated on single datasets or under controlled conditions, which limits their robustness under diverse real-world conditions. This paper presents a lightweight, end-to-end deep learning framework for speech-based pain level classification that explicitly targets multi-dataset robustness assessment. The proposed approach uses log-Mel spectrograms with an EfficientNetV2B0 backbone to learn discriminative acoustic features without relying on handcrafted feature engineering or multi-stage pipelines. The model is evaluated on heterogeneous datasets, including clinical recordings, controlled experimental data, and a More >