사물인터넷 응용을 위한 에지-포그 클라우드 기반 계층적 데이터 전달 방법의 설계 및 평가

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  • Author(s): 배인한1
  • Source:
    Journal of Internet Computing & Services. Feb2018, Vol. 19 Issue 1, p37-47. 11p.
  • Additional Information
    • Alternate Title:
      Design and Evaluation of an Edge-Fog Cloud-based Hierarchical Data Delivery Scheme for IoT Applications.
    • Abstract:
      The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery (EFcHD2) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed EFcHD2 method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of EFcHD2 method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods. [ABSTRACT FROM AUTHOR]
    • Abstract:
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