Xiaoxiong Yang^1,2, Dingde Jiang^1,*, Yi Zhang¹, Zhihan Lyu³

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083824 - 15 June 2026

Abstract The advancement of communication technology has made traffic engineering a critical issue in network systems. The traffic matrix is essential data that supports traffic engineering. The functionality of routing planning, network monitoring, and other modules within intelligent network management systems relies heavily on the network traffic matrix. However, real-time measurement of the network traffic matrix is costly and often suffers from missing or anomalous values. Consequently, long-term network traffic prediction presents significant challenges. Existing methods often fail to comprehensively address the multidimensional characteristics of traffic and the computational costs of the algorithms. To address these More >

Xiaoxiong Yang^1,2, Yi Zhang¹, Dingde Jiang^1,*, Shuqing He³

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083723 - 15 June 2026

Abstract With the proliferation of network users, traffic engineering has become increasingly important for the management and optimization of networks. As a crucial component of traffic engineering, the traffic matrix can assist network managers in making informed decisions to optimize resource utilization. However, in the current complex and heterogeneous space-ground integrated network, the cost of direct real-time measurement of traffic matrix is high and the delay is high. To address this challenge, we propose a network traffic estimation algorithm based on time-varying higher-order moments and deep learning, which leverages the time-varying higher-order moments property of traffic More >

Kanyang Jiang¹, Yingkai Kang², Ming Li^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083294 - 15 June 2026

Abstract The Internet of Vehicular Agents (IoVA) interconnects distributed AI agents across vehicular networks to deliver real-time intelligent services for vehicular users. Due to the limited computing capacity of vehicles, AI agents are deployed on nearby RoadSide Units (RSUs) to perform computation-intensive inference. As vehicles traverse RSU coverage boundaries, AI agents must migrate to target RSUs to maintain service continuity. However, the communication and computing resources at each RSU are shared among multiple co-served vehicles, creating coupled allocation decisions that jointly determine system latency and energy consumption. To address this challenge, we propose a low-latency and… More >

Yunkeun Song¹, Samuel Woo², Suji Lee³, Yousik Lee^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083267 - 15 June 2026

Abstract Software-Defined Vehicles (SDVs) increase cybersecurity complexity through the combination of external connectivity, software-intensive functions, and distributed development across vehicle manufacturers and suppliers. Although United Nations (UN) Regulation No. 155 and ISO/SAE 21434 require Threat Analysis and Risk Assessment (TARA) throughout the vehicle lifecycle, conventional TARA methodologies remain largely system-focused and often provide limited procedural guidance for coordinating supplier-derived TARA results at the vehicle level. This paper proposes an orchestration model for TARA across vehicle manufacturers and suppliers that structures TARA activities into the concept phase and the product development phases. The model defines interactions between… More >

Pan Li^1,2, Zhi Li³, Yingyou Wen^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083087 - 15 June 2026

Abstract The rapid advancement of edge intelligence in Industrial Internet of Things (IIoT) is transforming human–computer interaction from conventional “command execution” to complex “human–AI deep collaboration”. Within such safety-critical industrial environments, establishing robust mutual understanding and trust mechanisms becomes a significant prerequisite for decision reliability and efficiency. However, existing industrial interaction systems predominantly focus on task progression and explicit command responses, lacking fine-grained, dynamic tracking of operators’ trust states, cognitive evolution, and behavioral dynamics. Moreover, current LLM-based user simulation in evaluation often exhibit an “over-cooperation” bias, failing to capture the cognitive conflicts and trust crises characteristic… More >

Liangyu Lin, Li Feng^*, Lin Huang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.083047 - 15 June 2026

Abstract The modern sports streaming market is severely fragmented, forcing fans into costly, siloed platforms. While blockchain-based decentralized architectures offer a unified, interoperable sport streaming ecosystem, securely delivering commercial video over untrusted infrastructure remains a profound cryptographic challenge. Existing schemes fail to simultaneously support highly granular on-demand highlights and large scale dynamic live subscriptions. To resolve this, we propose a novel decentralized authorization architecture that systematically integrates existing cryptographic primitives into a decoupled three-layer protocol. By securely bridging on-chain state transitions with off-chain cryptographic enforcement, our architecture directly maps commercial payment workflows onto the underlying key More >

Abolfazl Saghafi^1,*, Soodeh Moallemian², Miray Budak², Rutvik Deshpande²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082996 - 15 June 2026

Abstract Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations. Generative Adversarial Imputation Networks (GAIN) offer a flexible deep-learning approach for missing value imputation, but their practical use is limited by convergence instability, sensitivity to hyperparameter selection, and dependence on outdated software implementations. To address these limitations, we propose Enhanced Generative Adversarial Imputation Networks (EGAIN), a modernized extension of GAIN implemented in TensorFlow 2.x. EGAIN incorporates convolution-based generator and discriminator networks, a channel-stacked representation of the data and mask, and checkpoint-based training diagnostics to improve stability More >

Anh Thi Diem Nguyen^1,2,#, Tham Vo¹, Vinh Truong Hoang^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082845 - 15 June 2026

Abstract Keeping customers engaged remains a major challenge in appointment-based services, where user behavior continuously shifts due to seasonal, market, and social factors. These dynamic changes often cause concept drift, rendering traditional deep clustering models unreliable because they assume stable data distributions. Most existing approaches handle representation learning, parameter optimization, and model updating as separate components, limiting their adaptability in real-world streaming environments. This study proposes Hybrid-RL, a novel adaptive clustering framework that unifies incremental deep representation learning, multi-head reinforcement learning for joint hyperparameter optimization (number of clusters, latent dimension, and clustering method), incremental model updating,… More >

Jiangyong Yu, Chuanping Hu^*, Runnan Wang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082810 - 15 June 2026

Abstract With the proliferation of Internet of Things (IoT) devices, accurate device fingerprinting of highly encrypted traffic has emerged as a critical challenge for ensuring network security. Existing deep learning models are either difficult to deploy in real-time due to excessive computational complexity (e.g., Transformers) or are limited in performance because their structure does not match the inherent hierarchy of traffic data (e.g., flattened state space models). Furthermore, a general lack of transparency in their decision-making processes restricts their trustworthiness in security-critical scenarios. To address these challenges, this paper proposes a Hierarchical Mamba with Gated Attribution More >

Jianfeng Liu¹, Yongjiao Yang¹, Kangyi Yang¹, Changhua Hu¹, Zijia Xu¹, Qingguo Shi², Yi Su^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082804 - 15 June 2026

Abstract Power enterprise inspection and supervision require greater intelligence, efficiency, and standardization; however, existing approaches are limited by inefficient knowledge retrieval, inaccurate issue identification, and insufficient support for standardized reporting and rectification tracking. This study proposes a lightweight, domain-adaptive large language model (LLM) framework based on Low-Rank Adaptation (LoRA), integrating Retrieval-Augmented Generation (RAG) and structured prompt engineering to enable evidence-grounded inspection tasks. The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base, enhancing output traceability, consistency, and task generalization. A key contribution is the introduction of a Sensitive Information Control Gate, More >