Biswanath Hajoary^1,*, Ranjay Das¹, Ganesh Roy², Daijiry Narzary³

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.075305 - 18 June 2026

Abstract The increasing integration of photovoltaic systems into smart grids requires accurate evaluation of power conversion efficiency and output performance. In this context, Z Source Multilevel Inverters function as voltage boosting converters and offer a certain degree of fault tolerance. However, conventional control strategies such as proportional integral controllers and hybrid optimization-based methods including POA-RFA (Pelican Optimization Algorithm-Random Forest Algorithm) are limited in their ability to maintain dynamic stability, efficiency, and operational safety under varying solar irradiance and load conditions. This study proposes a safe and explainable Deep Q Network based intelligent switching control framework for… More > Graphic Abstract

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 >

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 >

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 >

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 >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076726 - 09 April 2026

Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >

Fangxiang Hu¹, Qi Fu^1,2,*, Shiwen Zhang¹, Jing Huang¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074154 - 09 April 2026

Abstract In the Internet of Vehicles (IoV) environment, the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles. Traditional approaches, which rely on one or more computational resources within the cloud-edge-device computing model, struggle to ensure overall service quality when handling high-density traffic flows and large-scale tasks. To address this issue, we propose a computational offloading scheme based on a cloud-edge-device collaborative 6G IoV edge computing model, namely, Multi-Agent Deep Reinforcement Learning-based and Server-weighted scoring Selection (MADRLSS), which aims to optimize dynamic offloading decisions and resource allocation. The… More >

Mohammed Abdullah Alsuwaiket^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078169 - 30 March 2026

Abstract The healthcare field is fraught with challenges associated with severe class imbalance, wherein such critical conditions like sepsis, cardiac arrest, and drug adverse reactions are rare but have dire clinical consequences. This paper presents a new framework, Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events (DRAGON-MINE), to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events. The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an… More >