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 >

Muhammad Asim^1,*, Abdelhamied A. Ateya¹, Mudasir Ahmad Wani^1,2, Gauhar Ali¹, Mohammed ElAffendi¹, Ahmed A. Abd El-Latif¹, Reshma Siyal³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070684 - 12 January 2026

Abstract The growing developments in 5G and 6G wireless communications have revolutionized communications technologies, providing faster speeds with reduced latency and improved connectivity to users. However, it raises significant security challenges, including impersonation threats, data manipulation, distributed denial of service (DDoS) attacks, and privacy breaches. Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks. This survey provides a comprehensive review of how Federated Learning (FL), Blockchain, and Digital Twin (DT) technologies can collectively enhance the security of 5G and 6G systems. Blockchain offers decentralized, immutable, and transparent mechanisms for securing More >

Bohui Li¹, Bin Wang¹, Linjie Wu¹, Xingjuan Cai^1,*, Maoqing Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-28, 2026, DOI:10.32604/cmc.2025.070592 - 09 December 2025

Abstract Federated Learning (FL) provides an effective framework for efficient processing in vehicular edge computing. However, the dynamic and uncertain communication environment, along with the performance variations of vehicular devices, affect the distribution and uploading processes of model parameters. In FL-assisted Internet of Vehicles (IoV) scenarios, challenges such as data heterogeneity, limited device resources, and unstable communication environments become increasingly prominent. These issues necessitate intelligent vehicle selection schemes to enhance training efficiency. Given this context, we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions, and develop a dynamic interval multi-objective More >

Ateeqa Jalal^1,*, Umar Farooq^1,4,5, Ihsan Rabbi^1,4, Afzal Badshah², Aurangzeb Khan^1,4, Muhammad Mansoor Alam^3,4, Mazliham Mohd Su’ud^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068289 - 09 December 2025

Abstract The exponential growth of Internet of Things (IoT) devices, autonomous systems, and digital services is generating massive volumes of big data, projected to exceed 291 zettabytes by 2027. Conventional cloud computing, despite its high processing and storage capacity, suffers from increased network latency, network congestion, and high operational costs, making it unsuitable for latency-sensitive applications. Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership (TCO). Hybrid solutions, such as fog computing, cloudlets, and Mobile Edge Computing (MEC), attempt to balance cost and performance;… More >

Abdullah Alourani¹, Mehtab Alam^2,*, Ashraf Ali³, Ihtiram Raza Khan⁴, Chandra Kanta Samal²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161 - 10 November 2025

Abstract The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The… More >

Qingtao Meng, Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069970 - 10 November 2025

Abstract This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the… More >

Lincong Zhang¹, Yuqing Liu¹, Kefeng Wei², Weinan Zhao¹, Bo Qian^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069108 - 10 November 2025

Abstract In the field of edge computing, achieving low-latency computational task offloading with limited resources is a critical research challenge, particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications. In scenarios where edge servers are sparsely deployed, the lack of coordination and information sharing often leads to load imbalance, thereby increasing system latency. Furthermore, in regions without edge server coverage, tasks must be processed locally, which further exacerbates latency issues. To address these challenges, we propose a novel and efficient Deep Reinforcement Learning (DRL)-based approach aimed at minimizing average… More >

Fatima Al-Quayed^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069102 - 10 November 2025

Abstract Due to the growth of smart cities, many real-time systems have been developed to support smart cities using Internet of Things (IoT) and emerging technologies. They are formulated to collect the data for environment monitoring and automate the communication process. In recent decades, researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations. However, the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity. These systems are vulnerable to a variety of cyberattacks, including unauthorized access,… More >

Junjun Ren¹, Guoqiang Chen², Zheng-Yi Chai³, Dong Yuan^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068795 - 10 November 2025

Abstract Vehicle Edge Computing (VEC) and Cloud Computing (CC) significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit (RSU), thereby achieving lower delay and energy consumption. However, due to the limited storage capacity and energy budget of RSUs, it is challenging to meet the demands of the highly dynamic Internet of Vehicles (IoV) environment. Therefore, determining reasonable service caching and computation offloading strategies is crucial. To address this, this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading. By… More >

Zheng Yao^*, Puqing Chang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068248 - 10 November 2025

Abstract As Internet of Things (IoT) applications expand, Mobile Edge Computing (MEC) has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices. Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies, conflicting objectives, and limited resources. This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC. We jointly consider task heterogeneity, high-dimensional objectives, and flexible resource scheduling, modeling the problem as a Many-objective optimization. To solve it, we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on More >