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 >

Ho Hai Quan^1,2, Le Huu Binh^1,*, Nguyen Dinh Hoa Cuong³, Le Duc Huy⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075549 - 12 March 2026

Abstract Wireless Sensor Networks (WSNs) play a crucial role in numerous Internet of Things (IoT) applications and next-generation communication systems, yet they continue to face challenges in balancing energy efficiency and reliable connectivity. This study proposes SAC-HTC (Soft Actor-Critic-based High-performance Topology Control), a deep reinforcement learning (DRL) method based on the Actor-Critic framework, implemented within a Software Defined Wireless Sensor Network (SDWSN) architecture. In this approach, sensor nodes periodically transmit state information, including coordinates, node degree, transmission power, and neighbor lists, to a centralized controller. The controller acts as the reinforcement learning (RL) agent, with the… More >

Ahmad Salehiyan¹, Nuria Serrano², Francisco Hernando-Gallego³, Diego Martín^2,*, José Vicente Álvarez-Bravo²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075336 - 12 March 2026

Abstract The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection (FDI) attacks, posing critical threats to operational integrity, resource management, and public safety. Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated, stealthy threats. To address these challenges, we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework (Evo-Transformer-DRL) designed for robust and adaptive FDI detection in smart water infrastructures. The proposed architecture integrates three powerful paradigms: a transformer encoder for modeling complex temporal dependencies in multivariate time series, a… More >

Hamad Ali Abosaq¹, Jarallah Alqahtani^1,*, Fahad Masood², Alanoud Al Mazroa³, Muhammad Asad Khan⁴, Akm Bahalul Haque⁵

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075250 - 12 March 2026

Abstract The technological advancement of the vehicular Internet of Things (IoT) has revolutionized Intelligent Transportation Systems (ITS) into next-generation ITS. The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment. The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient, mobility-aware, and secure system for distributed intelligence. This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning (DRL-FL) approach to design an energy-efficient and threat-resilient ITS. In this approach, a Policy Proximal Optimization (PPO)-based DRL agent is first employed for adaptive client selection. Second, More >

Jinwei Mao^1,2, Wang Luo^1,2,*, Jiangtao Xu³, Daohua Zhu³, Wei Liang³, Zhechen Huang³, Bao Feng^1,2, Shuang Yang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072505 - 12 March 2026

Abstract With the rapid development of power Internet of Things (IoT) scenarios such as smart factories and smart homes, numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency. Multi-access edge computing technology deploys cloud computing capabilities at the network edge; constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability. Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,… More >

Lei Su^1,2,3, Wanli Feng^1,2,3, Cao Kan^1,2,3, Mingjiang Wei^1,2,3, Jihai Wang⁴, Pan Yu⁴, Lingxiao Yang^5,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071006 - 27 February 2026

Abstract To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources (DERs) (such as photovoltaic (PV) systems, wind turbines (WT), and energy storage (ES) devices), and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles (EVs) charging, this paper proposes a novel dual-scale hierarchical collaborative optimization strategy. This strategy decouples system-level economic dispatch from distributed EV agent control, effectively solving the resource coordination conflicts arising from the high computational complexity, poor scalability of existing centralized optimization, or the reliance on local information… More >

Yonghua Huo^1,2, Ying Liu^1,*, Anni Jiang³, Yang Yang³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072681 - 12 January 2026

Abstract With the advent of sixth-generation mobile communications (6G), space–air–ground integrated networks have become mainstream. This paper focuses on collaborative scheduling for mobile edge computing (MEC) under a three-tier heterogeneous architecture composed of mobile devices, unmanned aerial vehicles (UAVs), and macro base stations (BSs). This scenario typically faces fast channel fading, dynamic computational loads, and energy constraints, whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings. To address this issue, we formulate a multi-agent Markov decision process (MDP) for an air–ground-fused MEC system, unify link selection, bandwidth/power allocation, and task… More >

Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >