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  • Open Access


    MoTransFrame: Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

    Panyu Liu1, Huilin Ren2, Xiaojun Shi3, Yangyang Li4, *, Zhiping Cai1, Fang Liu5, Huacheng Zeng6

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2321-2334, 2020, DOI:10.32604/cmc.2020.010522

    Abstract Deep learning technology has been widely used in computer vision, speech recognition, natural language processing, and other related fields. The deep learning algorithm has high precision and high reliability. However, the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power. In this paper, we propose MoTransFrame, a general model processing framework for deep learning models. Instead of designing a model compression algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately. By the integration method,… More >

  • Open Access


    Edge-Computing with Graph Computation: A Novel Mechanism to Handle Network Intrusion and Address Spoofing in SDN

    Rashid Amin1, *, Mudassar Hussain2, Mohammed Alhameed3, Syed Mohsan Raza4, Fathe Jeribi3, Ali Tahir3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1869-1890, 2020, DOI:10.32604/cmc.2020.011758

    Abstract Software Defined Networking (SDN) being an emerging network control model is widely recognized as a control and management platform. This model provides efficient techniques to control and manage the enterprise network. Another emerging paradigm is edge computing in which data processing is performed at the edges of the network instead of a central controller. This data processing at the edge nodes reduces the latency and bandwidth requirements. In SDN, the controller is a single point of failure. Several security issues related to the traditional network can be solved by using SDN central management and control. Address Spoofing and Network Intrusion… More >

  • Open Access


    Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning

    Li Li1,*, Yifei Wei1, Lianping Zhang2, Xiaojun Wang3

    Journal of Cyber Security, Vol.2, No.3, pp. 141-150, 2020, DOI:10.32604/jcs.2020.010764

    Abstract The rapid growth of Internet content, applications and services require more computing and storage capacity and higher bandwidth. Traditionally, internet services are provided from the cloud (i.e., from far away) and consumed on increasingly smart devices. Edge computing and caching provides these services from nearby smart devices. Blending both approaches should combine the power of cloud services and the responsiveness of edge networks. This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies. We first analyze the scenarios of mobile edge networks with edge computing and caching abilities,… More >

  • Open Access


    DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

    Abdu Gumaei1, 2, *, Mabrook Al-Rakhami1, 2, Hussain AlSalman2, Sk. Md. Mizanur Rahman3, Atif Alamri1, 2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1033-1057, 2020, DOI:10.32604/cmc.2020.011740

    Abstract Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful… More >

  • Open Access


    Data Security Defense and Algorithm for Edge Computing Based on Mean Field Game

    Chengshan Qian1,2, Xue Li1,*, Ning Sun2, Yuqing Tian1

    Journal of Cyber Security, Vol.2, No.2, pp. 97-106, 2020, DOI:10.32604/jcs.2020.010548

    Abstract With the development of the Internet of Things, the edge devices are increasing. Cyber security issues in edge computing have also emerged and caused great concern. We propose a defense strategy based on Mean field game to solve the security issues of edge user data during edge computing. Firstly, an individual cost function is formulated to build an edge user data security defense model. Secondly, we research the More >

  • Open Access


    Resource Allocation in Edge-Computing Based Wireless Networks Based on Differential Game and Feedback Control

    Ruijie Lin1, Haitao Xu2, *, Meng Li3, Zhen Zhang4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 961-972, 2020, DOI:10.32604/cmc.2020.09686

    Abstract In this paper, we have proposed a differential game model to optimally solve the resource allocation problems in the edge-computing based wireless networks. In the proposed model, a wireless network with one cloud-computing center (CC) and lots of edge services providers (ESPs) is investigated. In order to provide users with higher services quality, the ESPs in the proposed wireless network should lease the computing resources from the CC and the CC can allocate its idle cloud computing resource to the ESPs. We will try to optimally allocate the edge computing resources between the ESPs and CC using the differential game… More >

  • Open Access


    Service Scheduling Based on Edge Computing for Power Distribution IoT

    Zhu Liu1, 2, *, Xuesong Qiu1, Shuai Zhang2, Siyang Deng2, Guangyi Liu3, *

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1351-1364, 2020, DOI:10.32604/cmc.2020.07334

    Abstract With the growing amounts of multi-micro grids, electric vehicles, smart home, smart cities connected to the Power Distribution Internet of Things (PD-IoT) system, greater computing resource and communication bandwidth are required for power distribution. It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence. This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT. The architecture, components and functional requirements of the PD-IoT with edge computing platform are proposed. Then, the structure of the service scheduling system is presented. Further, a… More >

  • Open Access


    Edge Computing-Based Tasks Offloading and Block Caching for Mobile Blockchain

    Yong Yan1, Yao Dai2, *, Zhiqiang Zhou3, Wei Jiang4, Shaoyong Guo2

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 905-915, 2020, DOI:10.32604/cmc.2020.07425

    Abstract Internet of Things (IoT) technology is rapidly evolving, but there is no trusted platform to protect user privacy, protect information between different IoT domains, and promote edge processing. Therefore, we integrate the blockchain technology into constructing trusted IoT platforms. However, the application of blockchain in IoT is hampered by the challenges posed by heavy computing processes. To solve the problem, we put forward a blockchain framework based on mobile edge computing, in which the blockchain mining tasks can be offloaded to nearby nodes or the edge computing service providers and the encrypted hashes of blocks can be cached in the… More >

  • Open Access


    User Profile System Based on Sentiment Analysis for Mobile Edge Computing

    Sang-Min Park1, Young-Gab Kim2, *

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 569-590, 2020, DOI:10.32604/cmc.2020.08666

    Abstract Emotions of users do not converge in a single application but are scattered across diverse applications. Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications. In this paper, we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity, media diversity, and server-side solution. Fine-grained data requires additional data and structural analysis in mobile devices. Media diversity requires standard parameters to integrate user data from various applications. A server-side… More >

  • Open Access


    Optimization of Face Recognition System Based on Azure IoT Edge

    Shen Li1, Fang Liu1,*, Jiayue Liang1, Zhenhua Cai1, Zhiyao Liang2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1377-1389, 2019, DOI:10.32604/cmc.2019.06402

    Abstract With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture “client-cloud” face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture.… More >

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