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WiFi MAC을 이용한 병원시설 인원계수의 활용에 관한 연구

A Study on People Count of Hospital Facilities Using Wi-Fi MAC

  • 류윤규 (대구보건대학교 스마트의료IT과)
  • Yun-Kyoo Ryoo (Department of Smart Medical IT, Daegu Health College)
  • 투고 : 2022.07.16
  • 심사 : 2022.12.16
  • 발행 : 2022.12.31

초록

사람들이 휴대하고 다니는 휴대폰에서 WiFi를 이용하여 MAC Address를 수집할 수 있음은 널리 알려진 사실이다. 하지만 개인의 동의없이 MAC Address를 수집하여 이용하는 것에 많은 법적인 문제가 수반되기 때문에 이를 적극 활용하기에는 많은 어려움이 있었다. 최근에 이러한 MAC Address가 의도치 않게 노출되어 사생활 정보가 침해되는 것을 방지하기 위하여 실제 기기의 MAC Address가 아니라 Random으로 생성된 가상의 MAC Address를 노출시켜 사생활 정보를 보호하는 방식이 모든 모바일 기기에 적용되고 있다. 기기에서 무작위하게 생성된 가상의 MAC 어드레스를 노출시킴으로써 MAC Address를 이용하여 개인을 특정하고 추적하는 것은 불가능하게 되었다. 하지만 MAC Address 수집을 통하여 여러 가지 사실을 추정할 수 있다는 사실은 여전히 유효하다. Random MAC Address가 확대 적용됨으로써 오히려 과거에 제기되어 오던 MAC Address를 이용한 사생활 정보 침해의 소지가 완전히 제거되어 과거보다 더욱 적극적으로 이를 활용할 수 있게 되었다. 본 논문에서는 MAC Address를 이용하여 의료진, 건물 관리인원, 환자, 환자의 가족 등의 병원시설의 이용자의 통계정보를 자동으로 수집하여 분석하는 방법을 제안한다. MAC Address를 이용한 병원시설 이용자의 수집은 비용이 저럼하고 상대적으로 매우 정확한 방법으로써 꾸준한 수집은 병원 운영에 매우 객관적이고 과학적인 근거자료를 제공할 수 있다.

It is a widely known fact that MAC addresses can be collected using Wi-Fi from mobile phones that people carry. However, since collecting and using MAC addresses without individual consent entails many legal problems, it was difficult to actively utilize them. In order to prevent invasion of privacy information due to unintentional exposure of these MAC addresses in recent years, the method of protecting privacy information by exposing a randomly generated virtual MAC address rather than the MAC address of the actual device is applied to all mobile devices. is becoming By exposing a randomly generated virtual MAC address on a device, it becomes impossible to identify and track an individual using the MAC address. However, the fact that various facts can be estimated through MAC Address collection is still valid. By expanding the application of Random MAC Address, the possibility of invasion of privacy information has been completely removed from the MAC address that has been raised in the past, so that it can be used more actively than in the past. In this paper, we propose a method for automatically collecting and analyzing statistical information of hospital visitors using MAC addresses. The collection of hospital visitors using MAC Address is a low cost and relatively accurate method, and the analysis of hospital visitors through steady collection can provide very objective and scientific evidence for hospital operation.

키워드

참고문헌

  1. Oppokhonov. Shokirkhon, J-H. Lee and J-Y. Moon, 'An Analysis of Wi-Fi Probe Request for Crowd Counting through MAC-Address classification', Journal of the Korea Institute of Information and Communication Engineering, 2022, 26.4: 612-623.  https://doi.org/10.6109/JKIICE.2022.26.4.612
  2. Y-K. Yoon, People Count For Managing Hospital Facilities, Journal of The Health Care and Life Science, 2020, vol.8, no.2, pp. 121-126 
  3. C-Y. Han and Y-K. Yoon, Development of hospital access control system using smartphone NFC, Journal of The Health Care and Life Science, vol.4, no.2, pp. 143-148, 2016. 
  4. S-H. Kwon, S-C. Lee and H-S. Kim, Development of congestion estimatio rpogram utilizing IEEE 802.11 Proberequest, KSCI summer Conference, Jeju university, 2021. 
  5. Jingwen Li, Lei Huang and Changping Liu, 'Robust people counting in video surveillance: Dataset and system, Advanced Video and Signal-Based Surveillance' 2011 8th IEEE International Conference(AVSS), pp. 54-59, 2011. 
  6. Jingyu Liu, Jiazheng Liu, and Mengyang Zhang, A detection and tracking based method for real-time people counting, Chinese Automation Congress (CAC) 2013, pp. 470-473, 2013. 
  7. Elvira Sukma Wahyuni, Rizqi Renafasih Alinra and Hendra Setiawan, 'People counting for indoor monitoring, Computing Engineering and Design', 2017 International Conference (ICCED), pp. 1-5, 2017. 
  8. Yang Cong, Haifeng Gong, Song-Chun Zhu and Yandong Tang, 'Flow mosaicking: Real-time pedestrian counting without scene-specific learning, Computer Vision and Pattern Recognition', 2009 IEEE Conference, pp. 1093-1100, 2009. 
  9. R Urtasun, DJ Fleet and P Fua, '3D people tracking with Gaussian process dynamical models', 2006 IEEE computer society Conference on Computer Vision and Pattern Recognition (CVPR), pp.238-245, 
  10. Y. Yang, J. Cao and X. Liu, "Wi-Count: Passing People Counting with COTS WiFi Devices," 2018 27th International Conference on Computer Communication and Networks (ICCCN), pp. 1-9, 2018. 
  11. Wei Xi, Jizhong Zhao, Xiangyang Li, K. Zhao, Shaojie Tang, Xue Liu and Zhiping Jiang, 'Electronic frog eye: Counting crowd using WiFi', IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, 2014. p. 361-369, 2014. 
  12. P. Reichl, B. Oh, R. Ravitharan and M. Stafford, "Using Wifi Technologies to Count Passengers in Real-time around Rail Infrastructure," 2018 International Conference on Intelligent Rail Transportation (ICIRT), pp. 1-5 IEEE, pp.1-5, 2018. 
  13. TOSI, Davide, MARZORATI and Stefano, 'Big data from cellular networks: real mobility scenarios for future smart cities', 2016 IEEE second international conference on big data computing service and applications (BigDataService). IEEE, pp.131-141, 2016.