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  • Open Access

    ARTICLE

    Proactive Caching at the Wireless Edge: A Novel Predictive User Popularity-Aware Approach

    Yunye Wan1, Peng Chen2, Yunni Xia1,*, Yong Ma3, Dongge Zhu4, Xu Wang5, Hui Liu6, Weiling Li7, Xianhua Niu2, Lei Xu8, Yumin Dong9

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1997-2017, 2024, DOI:10.32604/cmes.2024.048723

    Abstract Mobile Edge Computing (MEC) is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible. In an MEC environment, servers are deployed closer to mobile terminals to exploit storage infrastructure, improve content delivery efficiency, and enhance user experience. However, due to the limited capacity of edge servers, it remains a significant challenge to meet the changing, time-varying, and customized needs for highly diversified content of users. Recently, techniques for caching content at the edge are becoming popular for addressing the above challenges. It is capable of filling… More >

  • Open Access

    ARTICLE

    SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

    Jiehao Ye, Wen Cheng, Xiaolong Liu, Wenyi Zhu, Xuan’ang Wu, Shigen Shen*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2743-2769, 2024, DOI:10.32604/cmc.2024.049985

    Abstract The Internet of Things (IoT) has characteristics such as node mobility, node heterogeneity, link heterogeneity, and topology heterogeneity. In the face of the IoT characteristics and the explosive growth of IoT nodes, which brings about large-scale data processing requirements, edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions. However, the defense mechanism of Edge Computing-enabled IoT Nodes (ECIoTNs) is still weak due to their limited resources, so that they are susceptible to malicious software spread, which can compromise… More >

  • Open Access

    ARTICLE

    A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

    Yong Ma1, Han Zhao2, Kunyin Guo3,*, Yunni Xia3,*, Xu Wang4, Xianhua Niu5, Dongge Zhu6, Yumin Dong7

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 907-927, 2024, DOI:10.32604/cmes.2024.048759

    Abstract Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network’s periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions… More >

  • Open Access

    ARTICLE

    Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

    Wanbo Zhang1, Yuqi Fan1, Jun Zhang1, Xu Ding2,*, Jung Yoon Kim3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 863-885, 2024, DOI:10.32604/cmes.2024.047295

    Abstract Users and edge servers are not fully mutually trusted in mobile edge computing (MEC), and hence blockchain can be introduced to provide trustable MEC. In blockchain-based MEC, each edge server functions as a node in both MEC and blockchain, processing users’ tasks and then uploading the task related information to the blockchain. That is, each edge server runs both users’ offloaded tasks and blockchain tasks simultaneously. Note that there is a trade-off between the resource allocation for MEC and blockchain tasks. Therefore, the allocation of the resources of edge servers to the blockchain and the… More >

  • Open Access

    ARTICLE

    Task Offloading in Edge Computing Using GNNs and DQN

    Asier Garmendia-Orbegozo1, Jose David Nunez-Gonzalez1,*, Miguel Angel Anton2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2649-2671, 2024, DOI:10.32604/cmes.2024.045912

    Abstract In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and… More > Graphic Abstract

    Task Offloading in Edge Computing Using GNNs and DQN

  • Open Access

    ARTICLE

    Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

    Huixiang Xu*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2309-2335, 2024, DOI:10.32604/cmc.2024.046253

    Abstract The Internet of Things (IoT) has revolutionized how we interact with and gather data from our surrounding environment. IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights. The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation and connectivity. These IoT devices, equipped with many sensors and actuators, continuously produce vast volumes of data. However, the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges. However, transmitting… More >

  • Open Access

    ARTICLE

    IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

    Dinesh Mavaluru1,*, Chettupally Anil Carie2, Ahmed I. Alutaibi3, Satish Anamalamudi2, Bayapa Reddy Narapureddy4, Murali Krishna Enduri2, Md Ezaz Ahmed1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

    Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of… More >

  • Open Access

    ARTICLE

    A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

    Ahmed Barnawi1,*, Krishan Kumar2, Neeraj Kumar1, Bander Alzahrani1, Amal Almansour1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2117-2137, 2024, DOI:10.32604/cmes.2023.044184

    Abstract Landmines continue to pose an ongoing threat in various regions around the world, with countless buried landmines affecting numerous human lives. The detonation of these landmines results in thousands of casualties reported worldwide annually. Therefore, there is a pressing need to employ diverse landmine detection techniques for their removal. One effective approach for landmine detection is UAV (Unmanned Aerial Vehicle) based Airborne Magnetometry, which identifies magnetic anomalies in the local terrestrial magnetic field. It can generate a contour plot or heat map that visually represents the magnetic field strength. Despite the effectiveness of this approach,… More >

  • Open Access

    ARTICLE

    A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

    Ming Gao1,#, Weiwei Cai1,#, Yizhang Jiang1, Wenjun Hu3, Jian Yao2, Pengjiang Qian1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 259-277, 2024, DOI:10.32604/cmes.2023.029015

    Abstract Currently, applications accessing remote computing resources through cloud data centers is the main mode of operation, but this mode of operation greatly increases communication latency and reduces overall quality of service (QoS) and quality of experience (QoE). Edge computing technology extends cloud service functionality to the edge of the mobile network, closer to the task execution end, and can effectively mitigate the communication latency problem. However, the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management, and the booming development of artificial neural networks provides More >

  • Open Access

    ARTICLE

    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

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