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 >

Xianwei Li^1,2, Bo Wei^3,4, Xiaoying Yang^5,6,*, Amr Tolba⁷, Zijian Zeng⁸, Osama Alfarraj^7,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3437-3455, 2025, DOI:10.32604/cmc.2025.067200 - 23 September 2025

Abstract With the advancements of the next-generation communication networking and Internet of Things (IoT) technologies, a variety of computation-intensive applications (e.g., autonomous driving and face recognition) have emerged. The execution of these IoT applications demands a lot of computing resources. Nevertheless, terminal devices (TDs) usually do not have sufficient computing resources to process these applications. Offloading IoT applications to be processed by mobile edge computing (MEC) servers with more computing resources provides a promising way to address this issue. While a significant number of works have studied task offloading, only a few of them have considered More >

Qian Wang¹, Ya Zhou^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3835-3856, 2025, DOI:10.32604/cmc.2025.065465 - 03 July 2025

Abstract Edge computing has transformed smart grids by lowering latency, reducing network congestion, and enabling real-time decision-making. Nevertheless, devising an optimal task-offloading strategy remains challenging, as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions. We cast the offloading problem as a Markov Decision Process (MDP) and solve it with Deep Reinforcement Learning (DRL). Specifically, we present a three-tier architecture—end devices, edge nodes, and a cloud server—and enhance Proximal Policy Optimization (PPO) to learn adaptive, energy-aware policies. A Convolutional Neural Network (CNN) extracts high-level features from system states, enabling More >

Yiwei Zhang, Xin Cui^*, Qinghui Zhao

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2355-2373, 2025, DOI:10.32604/cmc.2025.065430 - 03 July 2025

Abstract The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed… More >

Jun Li^1,*, Yawei Dong¹, Liang Ni¹, Guopeng Feng¹, Fangfang Shan^1,2

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3537-3552, 2025, DOI:10.32604/cmc.2025.059325 - 16 April 2025

Abstract With the development of vehicle networks and the construction of roadside units, Vehicular Ad Hoc Networks (VANETs) are increasingly promoting cooperative computing patterns among vehicles. Vehicular edge computing (VEC) offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure, thereby reducing the computational burden on connected vehicles. However, this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes. Existing vehicular edge computing platforms have not adequately considered the misbehavior of vehicles. We propose a practical task offloading algorithm based on reputation assessment to More >

Aishwarya Rajasekar^*, Vetriselvi Vetrian

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 225-254, 2025, DOI:10.32604/cmes.2025.062642 - 11 April 2025

Abstract The integration of technologies like artificial intelligence, 6G, and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications. However, these advancements also generate a surge in data processing requirements, necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles. Despite recent advancements, the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources, as well as privacy, remain a concern. In this paper, a lightweight… More >

Usman Mahmood Malik¹, Muhammad Awais Javed², Ahmad Naseem Alvi², Mohammed Alkhathami^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2255-2274, 2025, DOI:10.32604/cmc.2025.061254 - 17 February 2025

Abstract Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing, and data storage services which are required for several 6G applications. Artificial Intelligence (AI) algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability. In this paper, the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers (POMH) in which larger tasks are divided into smaller subtasks and processed in parallel, hence expediting task completion. However, using POMH presents challenges… More >

Ru Hao^1,2,3, Chi Xu^2,3,*, Jing Liu¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3203-3222, 2025, DOI:10.32604/cmc.2024.059689 - 17 February 2025

Abstract Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely More >

Jiajia Liu^1,*, Peng Xie², Wei Li², Bo Tang², Jianhua Liu²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2609-2635, 2025, DOI:10.32604/cmc.2024.058810 - 17 February 2025

Abstract As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective… More >

Dayong Wang^1,*, Kamalrulnizam Bin Abu Bakar¹, Babangida Isyaku², Liping Lei³

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4465-4483, 2024, DOI:10.32604/cmc.2024.059616 - 19 December 2024

Abstract In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources.… More >