Chao He^1,*, Dongfeng Fu¹, Xin Xie², Jinkui Zhang³, Sirui Zhang⁴, Zheng Zhang⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085383 - 15 September 2026

Abstract With the rapid development of fifth generation (5G), 5G-Advanced, edge computing, and early sixth generation (6G) technologies, the Internet of Vehicles (IoV) is evolving toward highly connected, intelligent, and delay-sensitive transportation services. Nevertheless, ground infrastructure still faces coverage holes, blockage, overloaded roadside units (RSUs), limited backhaul, and weak service continuity in urban canyons, crowded intersections, long highway segments, rural roads, and emergency areas. Unmanned aerial vehicles (UAVs) can provide flexible aerial relay, mobile sensing, temporary coverage, and lightweight edge computing support for these scenarios. This survey reviews UAV-assisted IoV from an integrated sensing, communication, and… More >

Nidhika Chauhan^1,*, Navneet Kaur¹, Jawad Khan², Younhyun Jung², Haleem Farman³, Ahmed Sedik^3,4, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084063 - 15 September 2026

Abstract The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates… More >

Fei Bu¹, Zheng Wang^2,3,*, Yong Pan⁴, Zhaomin Wu¹, Yuchen Liang¹, Zhongshan Zhu⁴, Tengfei Tu⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083956 - 15 September 2026

Abstract Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge,… More >

Khamza Eshankulov¹, Bahodir Muminov², Robiya Farmonova¹, Dilnavoz Sodikova³, Bakhriddin Bozorov⁴, Zavqiddin Temirov⁵, Rashid Nasimov^6,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081803 - 23 July 2026

Abstract Cloud-based Business Intelligence (BI) systems operate under highly dynamic analytical workloads, including bursty OLAP queries, concurrent aggregations, and real-time microservice interactions, where static resource allocation leads to latency spikes and inefficient resource utilization. This paper proposes a decentralized adaptive Pareto-based multi-agent decision model for real-time resource coordination in cloud BI microservice environments. The agent placement problem is formulated as a multi-criteria decision process that minimizes service response latency, improves computational resource utilization, and preserves Quality-of-Service (QoS) stability. Instead of constructing a centralized global optimization policy, the proposed framework relies on decentralized locally Pareto-efficient decisions combined 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 >

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 >

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 >

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 >

Saira Batool^*, Muhammad Imran^*, Brett McKinney^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079067 - 30 March 2026

Abstract Bacterial growth requires strategic allocation of limited intracellular resources, especially under cold stress, where stabilized messenger ribonucleic acid (mRNA) secondary structures slow translation by impairing ribosome binding. Escherichia coli (E. coli) counters this bottleneck by inducing the cold-shock protein A (CspA), an RNA chaperone that remodels inhibitory structures. However, synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism, creating a fundamental resource-allocation trade-off. In this work, we develop a dynamical model capturing the interplay between metabolic precursors, ribosomes, and CspA, and use it to examine how growth and allocation patterns shift with temperature. Steady-state analysis shows… 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 >