Danilo Greco^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082708 - 15 June 2026

Abstract The Enhanced Graph Neural Network Autoencoder (Enhanced GNN-AE), recently proposed for unsupervised cybersecurity monitoring in battery energy storage systems (BESSs), builds a multiscale k-nearest neighbour graph over measurement samples and learns compact latent representations via manifold-regularised training. Its spatial encoder, however, employs the original Graph Attention Network (GAT), which has been formally shown to compute a rank-1 attention function equivalent to graph convolutional networks on many graph structures. This work investigates whether replacing the GAT encoder with the strictly more expressive GATv2 formulation—which applies the attention vector after a joint, asymmetric linear transformation of source… More >

Anping Wan^1,2,3, Yingchang Gao^1,3, Weikang Liu¹, Rui Yin¹, Khalil Al-Bukhaiti^1,3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081719 - 15 June 2026

Abstract Vertical roller mills are essential for energy-intensive grinding in cement, minerals, and metallurgy industries, consuming up to 50% of plant electricity and frequently experiencing operational instabilities (including excessive vibration and main motor current fluctuations) that drive unplanned downtime, increased wear, and reduced throughput. Despite their importance, real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure, feed rate, separator speed, and aerodynamic factors, which limit traditional control strategies under varying loads. This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence (AI).… More >

Peiying Zhang^1,2, Yihong Yu^1,2, Lizhuang Tan^3,4,*, Shuqing He⁵, Jian Wang⁶, Ameer El-Sayed⁷

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081224 - 15 June 2026

Abstract As a core information infrastructure in the 6G era, the Space-Air-Ground Integrated Network (SAGIN) integrates space-based, air-based, and ground-based network resources to achieve seamless communication across all domains. However, its characteristics such as heterogeneous node coupling and dynamic topology changes make it prone to cascading failures, severely threatening critical business continuity in Internet of Things (IoT) applications spanning smart cities, healthcare, transportation, and industrial automation. This paper conducts systematic research addressing challenges including modeling difficulties in SAGIN cascading failure propagation, insufficient coordination of defense strategies, and poor resource adaptability. First, a multi-factor coupled dynamic model… More >

Bhanu Talwar^1,*, Puneet Thapar¹, Tahani Alsubait², Mai Alduailij³, Ateeq Ur Rehman^4,*, Salil Bharany⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080977 - 15 June 2026

Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080961 - 15 June 2026

Abstract This review systematically analyzes Reinforcement Learning approaches for self-healing in energy-constrained secure edge IoT networks across 82 studies from 2020 to 2026. Unlike existing surveys that focus on general RL applications, the proposed review focuses on a three-level taxonomy that uniquely addresses edge IoT deployment realities through formulation-scope-hardware mapping. The work develops a novel three-level taxonomy classifying recovery scope (node, link, service, network), RL formulations (tabular, deep, multi-agent, model-based), and constraint integration (energy, latency, security, hybrid), revealing service migration dominance at 30% coverage and node recovery achieving 38% maximum energy savings. Normalized performance baselines establish More >

Manjodh Kaur¹, Princy Randhawa^2,*, Jitendra Jaiswal², Deepak Dubal³, Ravindra N. Bulakhe^4,5, Deepanraj Balakrishnan⁶, Nithesh Naik^7,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079503 - 15 June 2026

Abstract This review emphasizes the growing role of artificial intelligence (AI) in transforming the materials discovery process into a data-driven and autonomous approach. It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning, generative models, and AI agents are revolutionizing the design, screening, and optimization of materials. A key contribution is a detailed, step-by-step machine learning framework that guides researchers through data collection, preprocessing, feature engineering, model development, and validation, utilizing publicly available materials databases and computational tools. Additionally, the review discusses the latest advances in generative AI and autonomous More >

Ambreen Memon¹, Aaron Bere¹, Muhammad Nadeem Ali², Byung-Seo Kim^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078988 - 15 June 2026

Abstract The modern internet infrastructure has enabled numerous applications by providing a seamless connectivity experience across each mode of connectivity. Infrastructure-based connectivity and device-to-device (D2D) are well-known connectivity modes for internet-based applications. The selection of the underlying communication medium significantly affects energy consumption during data transfer. This study proposes an Energy-Efficient Data Dissemination Approach (EEDDA) that integrates encounter prediction with a multi-criteria decision-making (MCDM) framework to reduce infrastructure-based energy consumption in IoT mobility environments. Unlike traditional optimization approaches that focus on single-objective routing or static network models, the proposed framework dynamically selects between Device-to-Device (D2D) and More >

Zhadyra Toreniyaz¹, Guzal Ismailova¹, Oleg Prikhodko¹, Zhasulan Nakysbekov¹, Tilek Kuanyshbekov², Nurkadam Tolep¹, Dmitry Terekhov³, Zhandos Tolepov^1,*

Chalcogenide Letters, Vol.23, No.5, 2026, DOI:10.32604/cl.2026.081101 - 02 June 2026

Abstract Amorphous selenium (a-Se) thin films were deposited by vacuum thermal evaporation and investigated in planar photoconductive structures to evaluate their optical and photoelectrical properties in the low-field regime. SEM analysis showed continuous film coverage with a thickness of about 250 nm. Raman spectroscopy and X-ray diffraction confirmed the amorphous structure of the as-deposited films and the formation of trigonal crystalline selenium after annealing. Optical analysis based on transmission and reflectance spectra yielded an optical band gap E g = 1.96   eV and an Urbach energy E U = 0.083   eV ,… More >

Alanoud Al Mazroa¹, Fahad Masood², Bakri Hussain Awaji³, Mohammad Alhefdi⁴, Abeer Aljohani⁵, Jawad Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080134 - 27 May 2026

Abstract The rapid integration of Internet of Things (IoT) devices and distributed energy resources into smart grids has improved monitoring, control, and energy efficiency. However, it also exposes the grid to cyberattacks and privacy risks, as increased connectivity and data exchange can significantly disrupt energy management and system stability. Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks. This study proposes a privacy-preserving and cyber-resilient energy-optimization framework, FedGNN, for IoT-enabled smart grids that jointly integrates federated learning, graph neural network-based trust inference, and trust-aware energy dispatch.… More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Chedia Jarray², Huda Althumali³, Faten A. Saif ⁴, Raja Azlina Raja Mahmood¹, Mohammed Sani Adam⁵, Nor Fadzilah Abdullah⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077054 - 27 May 2026

Abstract Non-terrestrial networks (NTNs)—including UAVs, HAPs, and satellite systems—are rapidly becoming key enablers of wide-area, resilient connectivity for large-scale IoT applications. As these platforms integrate with terrestrial networks to form space–air–ground architectures, optimization challenges related to trajectory, resource management, energy efficiency, and security become increasingly complex. Machine learning (ML) has emerged as a central tool for addressing these challenges by enabling adaptive, data-driven decision-making under uncertainty. This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets. Using a structured methodology involving dataset curation, keyword filtering, metadata analysis, and citation-based paper selection,… More >