한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권2호
  • /
  • Pages.258-264
  • /
  • 2022
  • /
  • 2234-4772(pISSN)
  • /
  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

스마트 축사를 위한 에지 컴퓨팅 기반 IoT 협업 시스템

IoT Collaboration System Based on Edge Computing for Smart Livestock System

  • Ahn, Chi-Hyun (Department of Electronics and Communications Engineering, Kwangwoon University) ;
  • Lee, Hyungtak (School of Computer & Information Engineering, Kwangwoon University) ;
  • Chung, Kwangsue (Department of Electronics and Communications Engineering, Kwangwoon University)
  • 투고 : 2021.11.23
  • 심사 : 2021.12.29
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

정보 통신 기술 (ICT)이 축사와 접목된 스마트 축사는 대부분 클라우드 컴퓨팅 패러다임에 기반하고 있다. 클라우드 기반 스마트 축사는 응답 시간 증가, IoT 센서 증가에 따른 클라우드의 자원 부담, 망의 트래픽 부담과 같은 단점이 있고 인접한 IoT 디바이스와의 협업을 통한 장애 회복 메커니즘이 거의 없는 실정이다. 본 논문에서는 에지 컴퓨팅 기반 IoT 협업 시스템을 제안한다. 에지 디바이스의 비교적 제한적인 컴퓨팅 자원으로 클라우드의 웹 서버 기능을 분담하게 하여 클라우드에 필요한 자원을 절감하며, 사용자 요청에 대한 응답 시간을 개선하고자 한다. 또한 heartbeat 기반 장애 회복 메커니즘을 통하여 IoT 디바이스의 장애를 감지하고 그에 따른 적절한 조치를 하도록 하였다.

The smart farm for livestock, in which information and communication technology (ICT) is combined with livestock farm, is mostly based on the cloud computing paradigm. A cloud-based smart livestock farm has disadvantages such as increased response time, burden on cloud resource caused by the increased number of IoT sensors, traffic burden on the network, and lack of failure resilience mechanisms through collaboration with adjacent IoT devices. In this paper, with these problems in mind, we propose an IoT collaboration system based on edge computing. By using the relatively limited computing resources of the edge device to share the cloud's web server function, we aim to reduce the cloud's resources needed and improve response time to user requests. In addition, through the heartbeat-based failure recovery mechanism, IoT device failures were detected and appropriate measures were taken.

키워드

과제정보

The work reported in this paper was conducted during the sabbatical year of Kwangwoon University in 2021.

참고문헌

  1. Z. Chen, G. Xu, V. Mahalingam, L. Ge, J. Nguyen, W. Yu, and C. Lu, "A Cloud Computing based Network Monitoring and Threat Detection System for Critical Infrastructures," Big Data Research, vol. 3, pp. 10-23, Apr. 2016. https://doi.org/10.1016/j.bdr.2015.11.002
  2. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, Oct. 2016. https://doi.org/10.1109/JIOT.2016.2579198
  3. A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel, "The Cost of a Cloud: Research Problems in Data Center Networks," ACM SIGCOMM Computer Communication Review, vol. 39, no. 1, pp. 68-73, Dec. 2008.
  4. E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, "MAUI: Making Smartphones Last Longer with Code Offload," in International Conference on Mobile Systems, Applications, and Services, pp. 49-62, Jun. 2010.
  5. M. Babar, M. S. Khan, F. Ali, M. Imran, and M. Shoaib, "Cloudlet Computing: Recent Advances, Taxonomy, and Challenges," IEEE Access, vol. 9, pp. 29609-29622, 2021. https://doi.org/10.1109/ACCESS.2021.3059072
  6. R. Mahmud, R. Kotagiri, and R. Buyya, "Fog Computing: A Taxonomy, Survey and Future Directions," Internet of Everything. Springer, pp. 103-130, Oct. 2018.
  7. J. Ren, G. Yu, Y. He, and G. Y. Li, "Collaborative Cloud and Edge Computing for Latency Minimization," IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031-5044, May. 2019. https://doi.org/10.1109/tvt.2019.2904244
  8. G. Premsankar, M. Di Francesco, and T. Taleb, "Edge Computing for the Internet of Things: A Case Study," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1275-1284, Apr. 2018. https://doi.org/10.1109/jiot.2018.2805263