Priyanka Halder, Gopikrishnan Sundaram^*

Journal on Internet of Things, Vol.8, pp. 127-153, 2026, DOI:10.32604/jiot.2026.073962 - 29 May 2026

Abstract The need for intelligent learning frameworks that can function under stringent limitations relating to privacy, energy, scalability, and trust has increased due to the Internet of Things’ (IoT) and the Internet of Artificial Things’ (IoAT) explosive expansion. Federated Learning (FL), which allows collaborative model training without sharing raw data, has become a potential approach. Non-IID data delivery, inconsistent client engagement, vulnerability to poisoning assaults, and low resource knowledge are among of the significant obstacles that FL alone must overcome. Blockchain integration adds extra overhead in terms of latency, energy consumption, and scalability, but it has… More >

Yousra Abudaqqa^*, Zulaiha Ali Othman, Azuraliza Abu Bakar

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079034 - 27 May 2026

Abstract Signal categorization is a critical component of the Dendritic Cell Algorithm (DCA), as it directly influences its anomaly detection capability. Conventional DCA implementations typically rely on heuristic or optimization-based approaches, such as Grouping Particle Swarm Optimization (GPSO), Grouping Genetic Algorithms (GGA), Principal Component Analysis (PCA), and Support Vector Machines (SVM), to determine mappings between input features and the three immunological signal categories: Pathogen-Associated Molecular Patterns (PAMP), Danger Signals (DS), and Safe Signals (SS). These approaches depend heavily on domain expertise and predefined rules, making the resulting signal mappings static and often dataset specific. Consequently, the… 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 >

Computers, Materials & Continua Editorial Office

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.083414 - 08 May 2026

Abstract This article has no abstract. More >

Chengjing Li¹, Li Wang^2,*, Xiaoyan Zhao²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079959 - 08 May 2026

Abstract Robots perform diverse tasks in real-world scenarios. In safety-critical applications, robot control must prioritize satisfying safety constraints in addition to achieving high performance. Offline safe reinforcement learning avoids risky online exploration by training from a given dataset. However, most existing methods overlook two issues in offline data. First, non-zero cost signals are typically sparse, which leads to inaccurate cost value estimates and makes it difficult to impose effective safety constraints on the policy. Second, an imbalanced dataset biases policy learning toward unsafe behaviors. To address these challenges, we propose an actor-critic method ARMOR (multi-scAle Reweighting with Multi-task… More >

Jinchuan Pei¹, Yuxiang Hu^1,2,3,4,*, Hongtao Yu¹, Zihao Wang¹, Menglong Li^1,2,3,4

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079684 - 08 May 2026

Abstract In the cloud-edge collaborative network, advanced persistent threats (APTs) pose a serious security risk to critical network assets. Although network deception defense can mislead attackers’ cognition, its effectiveness depends on dynamically selecting appropriate rotation timings of the deception defense. However, the deployment of deception resources and state updates is not completed instantaneously, and existing methods ignore the state transition delay and the dynamic interaction between the attackers and defenders during the real attack and defense process. To address this, we propose a deception defense timing selection method based on the time-delayed FlipIt game. Firstly, a… More >

Zongchuan Li, Chen Sun^*, Jian Shu

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079214 - 08 May 2026

Abstract This paper investigates the joint resource allocation problem in Reconfigurable Intelligent Surface (RIS)-assisted cooperative non-orthogonal multiple access device-to-device (CNOMA-D2D) cellular networks. To tackle the high-dimensional non-convex joint optimization of power control, RIS phase configuration and channel assignment, we propose an integrated user pairing strategy, PIP-UP, quantifying utility through factors, phase alignment, interference suppression and power difference, neglected in existing methods. Furthermore, we develop a hybrid deep reinforcement learning algorithm, A3TD, combining the parallel exploration capability of Asynchronous Advantage Actor-Critic (A3C) with the stable continuous optimization of Twin Delayed Deep Deterministic Policy Gradient (TD3). This integration More >

Chaobin Wang^1,2, Xianghong Tang^1,2,*, Jianguang Lu^1,2, Jing Yang^1,2, Panliang Yuan^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078998 - 08 May 2026

Abstract Mobile Edge Computing (MEC) facilitates the rapid response and energy-efficient execution of tasks on mobile devices. However, determining whether and where to offload tasks remains a significant challenge due to the constantly changing character of workloads in MEC environments. To address this issue, this paper proposes PreAlloc-A2C—a deep reinforcement learning actor-critic-based framework that calculates allocation scores by leveraging both task features (task size, required completion time, and waiting time) and server features (queue length and historical workload). This design enables fully distributed task offloading decisions without centralized coordination. Additionally, a Long Short-Term Memory (LSTM) network More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077510 - 09 April 2026

Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >

Kiseok Kim^#, Taehoon Yoo^#, Sangmin Lee, Hwangnam Kim^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077415 - 09 April 2026

Abstract Conventional Low-Rank Adaptation (LoRA) constrains weight updates to a static linear low-rank manifold, which is inherently limited when applied to Reinforcement Learning (RL) tasks for Unmanned Aerial Vehicle (UAV) applications. UAVs operate in highly dynamic and nonstationary environments where rapid variations in sensing and state transitions lead to complex, nonlinear input–output relationships. Such environmental complexity cannot be adequately modeled by a static Low-rank approximation, making conventional LoRA approaches insufficient for the high-dimensional dynamics required in UAV applications. To overcome these limitations, we propose an attention-enhanced LoRA that constructs an input-dependent and intrinsically nonlinear adaptation manifold.… More >