DOI QR코드

DOI QR Code

MongoDB를 활용한 풀 스택 플랫폼 설계

Full Stack Platform Design with MongoDB

  • 홍선학 (서일대학교 컴퓨터응용과) ;
  • 조경순 (서일대학교 컴퓨터응용과)
  • 투고 : 2016.07.25
  • 심사 : 2016.11.30
  • 발행 : 2016.12.25

초록

본 논문에서는 오픈소스 플랫폼 라즈베리파이 3 모델을 기반으로 몽고DB 데이터베이스를 활용하여 풀 스택 플랫폼을 구현하였다. 가속도 센서를 사용하여 무선 통신으로 데이터를 로깅하는 도구로써 이벤트 구동 방식을 사용하였으며, 리눅스 라즈비안 Jessie 버전으로 초당 28 프레임으로 USB 카메라(MS LifeCam 시네마) 이미지를 획득하며, 안드로이드 모바일 기기와 인터페이스를 구축하기 위하여 블루투스 통신 기술을 확장하였다. 따라서 본 논문에서는 가속도 센서 동작을 검출하여 이벤트 트리거링을 감지하는 풀 스택 플랫폼 기능을 구현하고, IoT 환경에서 온도와 습도 센서 데이터를 수집한다. 특히 몽고 DB가 MEAN 스택과 가장 좋은 데이터 연결성을 갖고 있기 때문에 풀 스택 플랫폼 성능을 개발 향상시키는데 MEAN 스택을 사용하였다. 향후 IoT 클라우드 환경에서 풀 스택 성능을 향상시키고, 몽고 DB를 활용하여 보다 쉽게 웹 설계 성능을 향상시키도록 기술을 개발하겠다.

In this paper, we implemented the full stack platform design with MongoDB database of open source platform Raspberry PI 3 model. We experimented the triggering of event driven with acceleration sensor data logging with wireless communication. we captured the image of USB Camera(MS LifeCam cinema) with 28 frames per second under the Linux version of Raspbian Jessie and extended the functionality of wireless communication function with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the full stack platform for recognizing the event triggering characteristics of detecting the acceleration sensor action and gathering the temperature and humidity sensor data under IoT environment. Especially we used MEAN Stack for developing the performance of full stack platform because the MEAN Stack is more akin to working with MongoDB than what we know of as a database. Afterwards, we would enhance the performance of full stack platform for IoT clouding functionalities and more feasible web design with MongoDB.

키워드

참고문헌

  1. Park Jae Ho, Modern App Development Basic with MEAN Stack, Acorn, 02.2015.
  2. Son Byung Dae, Node Web Development, , Acorn. 11, 2011.
  3. https://nodejs.org/documentation/Tutorials
  4. Hong. Sun Hag and Choi Young Woo, Opensource MongoDB with studying Node.js & fluentd, SungAnDang, 03, 2016
  5. Leo Breiman, "Random Forest, Machine Learning," vol. 45, 2001, pp5-32. https://doi.org/10.1023/A:1010933404324
  6. Paul Viola and Michael J. Jones, "Robust real-time face detection," International Journal of Computer Vision, Vol 57, No2, 2004, pp137-154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  7. Hong Sun Hag, "Monbile Embedded USN Platform Design" Korea Institute of Communications and Information Sciences, 37 vol, 4th, 08, 2012.
  8. Oh Il Suk, Computer Vision-From Basic to Recent Mobile Application, Hanbit Media, 07, 2014
  9. Hong Sun Hag, Cho Kyung Soon, "Computer Vision Platform Design with MEAN Stack," Korea Society of Digital Industry & Information Magement, 11 vol, 3th, 2015, 09, pp. 79-87.
  10. Hong Sun Hag, , "Mobile Arduino Embedded Platform Design," Korea Society of Digital Industry & Information Magement, 9 vol, 4th, 12, 2013, pp. 33-41
  11. Kim Young Ro, Cho Young Tae, Ryu Seung Gi "Pothole Detection using Intensity and Motion Information", Institute of Electronics and Information Engineers, 52(11), 2015.11 pp137-146.
  12. Seo Choo Won,"The Image Position Measurement for the Selected Object out of the Center using the 2 Points Polar Coordinate Transform" Institute of Electronics and Information Engineers, 52(11), 2015.11 pp147-155.
  13. Mongo DB Web, http://docs.mongodb.org/manual/, 08, 2015.
  14. Michael McTear, Callejas Zoraida, Voice Application Development for Android, Acorn, 08.2014.
  15. Hong Sun Hag, Arduino following with Mobile texhniques, SungAnDang, 04, 2013
  16. Wolfgang Mauerer, Professional Linux Kernel architecture, Wiely Pub, 2007.
  17. Downey, Allen B, Python for software Design, Cambridge, 03, 2009
  18. Simon Monk, RapsberryPi Cookbook, HanBit Media, 01, 2015
  19. Robert Lagani Re, OpenCV 2 Computer Vision Application Programming Cookbook, Acorn, 04, 2012
  20. Computer Bookshops, Opencv Computer Vision Application Programming Cookbook (2nd Edition), Packet Pub.03, 2013
  21. Drew Conway, John Myels White, "Machine Learning for Hackers", Insight, 01, 2014.