• Title/Summary/Keyword: link cost function

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Efficient IoT data processing techniques based on deep learning for Edge Network Environments (에지 네트워크 환경을 위한 딥 러닝 기반의 효율적인 IoT 데이터 처리 기법)

  • Jeong, Yoon-Su
    • Journal of Digital Convergence
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    • v.20 no.3
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    • pp.325-331
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    • 2022
  • As IoT devices are used in various ways in an edge network environment, multiple studies are being conducted that utilizes the information collected from IoT devices in various applications. However, it is not easy to apply accurate IoT data immediately as IoT data collected according to network environment (interference, interference, etc.) are frequently missed or error occurs. In order to minimize mistakes in IoT data collected in an edge network environment, this paper proposes a management technique that ensures the reliability of IoT data by randomly generating signature values of IoT data and allocating only Security Information (SI) values to IoT data in bit form. The proposed technique binds IoT data into a blockchain by applying multiple hash chains to asymmetrically link and process data collected from IoT devices. In this case, the blockchainized IoT data uses a probability function to which a weight is applied according to a correlation index based on deep learning. In addition, the proposed technique can expand and operate grouped IoT data into an n-layer structure to lower the integrity and processing cost of IoT data.