Journals / CMC / Vol.,
Table of Content

Research Article

BEST PAPER 2021

Emerging Trends in Software-Defined Networking for Industry 4.0


Submission Deadline: 31 July 2021 (closed)

Abstract

This article has no abstract.

Keywords

Industry 4.0, Industrial IoT, Machine Learning, Software Defined Networks
  • Research Article

    BEST PAPER 2021

    Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network

    Mohammad Shoab, Abdullah Shawan Alotaibi* CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.021941
    Abstract Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Flow Management Mechanism in Software-Defined Network

    Eugene Tan, Yung-Wey Chong*, Mohammed F. R. Anbar CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1437-1459, 2022, DOI:10.32604/cmc.2022.019516
    Abstract Software-defined networking (SDN) is a paradigm shift in modern networking. However, centralised controller architecture in SDN imposed flow setup overhead issue as the control plane handles all flows regardless of size and priority. Existing frameworks strictly reduce control plane overhead and it does not focus on rule placement of the flows itself. Furthermore, existing frameworks do not focus on managing elephant flows like RTSP. Thus, the proposed mechanism will use the flow statistics gathering method such as random packet sampling to determine elephant flow and microflow via a pre-defined threshold. This mechanism will ensure that the control plane works at… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Centralized QoS Routing Model for Delay/Loss Sensitive Flows at the SDN-IoT Infrastructure

    Mykola Beshley1, Natalia Kryvinska2,*, Halyna Beshley1, Mykhailo Medvetskyi1, Leonard Barolli3 CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3727-3748, 2021, DOI:10.32604/cmc.2021.018625
    Abstract The rapidly increasing number of Internet of Things (IoT) devices and Quality of Service (QoS) requirements have made the provisioning of network solutions to meet this demand a major research topic. Providing fast and reliable routing paths based on the QoS requirements of IoT devices is very important task for Industry 4.0. The software-defined network is one of the most current interesting research developments, offering an efficient and effective solution for centralized control and network intelligence. A new SDN-IoT paradigm has been proposed to improve network QoS, taking advantage of SDN architecture in IoT networks. At the present time, most… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

    M. Iyapparaja1, M. Sathish Kumar1, S. Siva Rama Krishnan1, Chiranji Lal Chowdhary1, Byungun Yoon2, Saurabh Singh2, Gi Hwan Cho3,* CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3163-3178, 2021, DOI:10.32604/cmc.2021.017302
    Abstract Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

    Anil Kumar Gulivindala1, M. V. A. Raju Bahubalendruni1, R. Chandrasekar1,2, Ejaz Ahmed2, Mustufa Haider Abidi3,*, Abdulrahman Al-Ahmari4 CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2531-2548, 2021, DOI:10.32604/cmc.2021.018014
    Abstract The evolution of Industry 4.0 made it essential to adopt the Internet of Things (IoT) and Cloud Computing (CC) technologies to perform activities in the new age of manufacturing. These technologies enable collecting, storing, and retrieving essential information from the manufacturing stage. Data collected at sites are shared with others where execution automatedly occurs. The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process. However, information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern. The current research validates the information optimally… More >

    Graphic Abstract

Share Link

WeChat scan