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Development of a Server-independent System to Identify and Communicate Fire Information and Location Tracking of Evacuees

화재정보 확인과 대피자 위치추적을 위한 서버 독립형 시스템 개발

  • Lee, Chijoo (Construction Economy & Industry Research Division, Korea Research Institute for Human Settlements) ;
  • Lee, Taekwan (Housing Works Business Team, Building Works Division, Hyundai Engineering & Construction Corporation)
  • Received : 2021.10.24
  • Accepted : 2021.12.01
  • Published : 2021.12.20

Abstract

If a fire breaks out in a building, occupants can evacuate more rapidly if they are able to identify the location of the fire, the exits, and themselves. This study derives the requirements of system development, such as distance non-limitation, a non-additional device, a non-centralized server system, and low power for an emergency, to identify information about the fire and the location of evacuees. The objective is to receive and transmit information and reduce the time and effort of the database for location tracking. Accordingly, this study develops a server-independent system that collects information related to a building fire and an evacuee's location and provides information to the evacuee on their mobile device. The system is composed of a transmitting unit to disseminate fire location information and a mobile device application to determine the locations of the fire and the evacuee. The developed system can contribute to reducing the damage to humans because evacuees can identify the location of the fire, exits, and themselves regardless of the impaired server system by fire, the interruption of power source, and the evacuee's location. Furthermore, this study proposes a theoretical basis for reducing the effort required for database construction of the k-nearest neighbor fingerprint.

화재가 발생했을 때, 대피자가 화재 위치와 규모 등의 화재정보, 그리고 출구 위치와 대피자 스스로의 위치를 확인할 수 있다면, 신속하게 대피할 수 있을 것이다. 본 연구에서는 화재정보를 대피자에게 전송하고 대피자의 위치를 확인할 수 있는 시스템을 개발하였다. 선행연구를 통하여 시스템 개발에 필요한 요구사항을 네 가지 도출하였다. 요구사항에는 시스템이 작동하는데 필요한 전력이 크지 않아야 하며, 화재정보를 송·수신하기 위해서 필요한 거리 제한과 추가 장비, 그리고 중앙 서버가 없어도 시스템이 작동할 수 있어야 한다는 것이 포함된다. 이와 같은 요구사항을 기반으로, 본 연구에서는 건물 화재정보를 대피자의 모바일 기기로 전송할 수 있고, 대피자의 위치를 추적할 수 있는 서버 독립형 시스템을 개발하였다. 개발된 시스템은 화재정보를 전송하는 장치와 화재정보를 수신하고 대피자 위치를 추적할 수 있는 모바일 기기의 어플리케이션으로 구성된다. 화재에 의해서 중앙 서버가 손상되어도 대피자는 개발된 시스템을 사용하여 화재 위치와 규모, 출구의 위치와 대파자의 위치를 확인할 수 있으므로, 인명피해를 감소시키는데 기여할 수 있을 것이다. 또한, 실내위치추적에 사용되는 fingerprint 알고리듬의 사용성 향상을 위한 이론적 기초로도 활용될 수 있을 것이다. Fingerprint 사용을 위한 데이터베이스를 구축할 때에 소요되는 노력과 비용을 감소시키는 방법을 제안했기 때문이다.

Keywords

Acknowledgement

The authors express their thanks to Professor Ghang Lee (Department of Architectural Engineering, Yonsei University, South Korea) for his advice and generosity about information offering.

References

  1. Status of fire occurrence by city and province [Internet]. Daejeun (Korea): Korean Statistical Information Service(KOSIS). 1997 - [cited 2021 Oct 27]. Availble from: https://kosis.kr/statHtml/statHtml.do?orgId=156&tblId=TX_15601_A004&conn_path=I3 2021
  2. Park HJ, Meacham BJ, Dembsey NA. Goulthorpe M. Enhancing building fire safety performance by reducing miscommunication and misconceptions. Fire Technology. 2014 Nov;50:183-203. https://doi.org/10.1007/s10694-013-0365-2
  3. Park HH, Ju DH, Kim YS. A system of safety management by using bluetooth and NFC: By protecting construction workers in safety accidents. Proceedings of Korean Journal of Construction Engineering and Management; 2016 Nov. 12; Incheon, South Korea. Seoul (Korea): Korea Institute of Construction Engineering and Management; 2016. p. 19-22.
  4. Lee JK, Lee YH, Park JH, Son MJ. Experimental study on wall transmission loss of electric wave for the RTLS application of building construction project. Journal of Korea Institute of Building Construction. 2009 Feb;9(1): 95-101. https://doi.org/10.5345/JKIC.2009.9.1.095
  5. Arias S, Mendola SL, Wahlqvist J, Rios O, Nilsson D, Ronchi E. Virtual reality evacuation experiments on way-finding systems for the future circular collider. Fire Technology. 2019 May;55:2319-40. https://doi.org/10.1007/s10694-019-00868-y
  6. Imanishi M, Sano T. Route choice and flow rate in theatre evacuation drills: Analysis of walking trajectory data-set. Fire Technology. 2019 Oct;55:569-93. https://doi.org/10.1007/s10694-018-0783-2
  7. Abid F. A survey of machine learning algorithms based forest fires prediction and detection systems. Fire Technology. 2020 Nov;57:559-90. https://doi.org/10.1007/s10694-020-01056-z
  8. Silvani X, Morandini F, Innocenti E, Peres S. Evaluation of a wireless sensor network with low cost and low energy consumption for fire detection and monitoring. Fire Technology. 2014 Oct;51:971-93. https://doi.org/10.1007/s10694-014-0439-9
  9. Lee TK. Development of a mobile-based fire evacuation system using a wireless network [master thesis]. [Seoul (Korea)]: Yonsei University. 2013. 36 p.
  10. Dibley M. Li H, Rezgui Y, Miles J. An integrated framework utilising software agent reasoning and ontology models for sensor based building monitoring. Journal of Civil Engineering and Management. 2015 Feb;21:356-75. https://doi.org/10.3846/13923730.2014.890645
  11. Jin BR, Yu DH. Analysis for the application of Fingerprinting method with Wi-Fi RSSI provided in the public DB. Proceedings of Symposium of the Korean Institute of communications and Information Sciences; 2021 Feb 3~5; Gangwon-do, South Korea. Seoul (Korea): the Korean Institute of communications and Information Sciences; 2021. p. 933-4.
  12. Filippoupolitis A, Gorbil G, Gelenbe E. Autonomous navigation systems for emergency management in buildings. IEEE Globecom Workshops; 2011 Dec 5-9; Houston, TX, USA. New Jersey (US): Institute of Electrical and Electronics Engineers; 2011. p. 1056-61. https://doi.org/10.1109/GLOCOMW.2011.6162338
  13. Chu L, Wu SJ. A real-time fire evacuation system with cloud computing. Journal of Convergence Information Technology. 2012 Apr;7(7):208-15. https://doi.org/10.4156/jcit.vol7.issue7.26
  14. Aedo I, Yu S, Diaz P, Acuna P, Onorati T. Personalized Alert Notifications and Evacuation Routes in Indoor Environments. Journal of Sensors. MDPI. 2012 Jun;12(6):7804-27. https://doi.org/10.3390/s120607804
  15. Rakip KI, Fatmagul B, Alias AR. An evacuation system for extraordinary indoor air pollution disaster circumstances. Disaster Advances. 2012 Apr;5(2):33-40.
  16. Zdruba GV, Huber M, Karnangar FA, Chlarntac I. Monte carlo sampling based in-home location tracking with minimal RF infrastructure requirements. IEEE Global Telecommunications Conference; 2004 Nov 29-Dec 3; Dallas, TX. New Jersey (US): Institute of Electrical and Electronics Engineers; 2004. p. 3624-29. https://doi.org/10.1109/GLOCOM.2004.1379045
  17. Tayebi A, Gomez J, Saez de Adana F, Gutierrez O. The application of ray tracing to mobile localization using the direction of arrival and received signal strength in multipath indoor environments. Progress In Electromagnetics Research. 2009 Jan;91:1-15. https://doi.org/10.2528/PIER09020301
  18. El-Kafrawy K, Youssef M, El-Keyi A, Naguib A. Propagation modeling for accurate indoor WLAN RSS-based localization. 2010 IEEE 72nd Vehicular Technology Conference; 2010 Sep 6-9; Ottawa, Canada. New Jersey (US): Institute of Electrical and Electronics Engineers; 2010. p. 1-5. https://doi.org/10.1109/VETECF.2010.5594108
  19. Narzullaev A, Park Y, Yoo K, Yu J. A fast and accurate calibration algorithm for real-time locating systems based on the received signal strength indication. AEU - International Journal of Electronics and Communications. 2011 Apr;65(4):305-11. https://doi.org/10.1016/j.aeue.2010.03.012
  20. Campos RS, Lovisolo L, de Campos MLR. Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity. Expert Systems with Applications. 2014 Oct;41(14):6211-23. https://doi.org/10.1016/j.eswa.2014.04.011
  21. Zhou M, Tian Z, Xu K, Yu X, Hong X, Wu H. SCaNME: Location tracking system in large-scale campus Wi-Fi environment using unlabeled mobility map. Expert Systems with Applications. 2014 Jun;41:3429-43. https://doi.org/10.1016/j.eswa.2013.10.047
  22. Yim JG. Park CS. Joo JH. Jeong SH. Extended kalman filter for wireless LAN based indoor positioning. Decision Support Systems. 2008 Nov;45(4):960-71. https://doi.org/10.1016/j.dss.2008.03.004
  23. Noh HY, Lee JH, Oh SW, Hwang KS, Cho SB. Exploiting indoor location and mobile information for context-awareness service. Information Processing & Management. 2012 Jan;48(1):1-12. https://doi.org/10.1016/j.ipm.2011.02.005
  24. Woo SK, Jeong SS, Mok E, Xia L, Choi CS, Pyeon MW, Heo J. Application of WiFi-based indoor positioning system for labor tracking at construction sites: A case study in Guangzhou MTR. Automation in Construction. 2011 Jan;20(1):3-13. https://doi.org/10.1016/j.autcon.2010.07.009
  25. Youssef M. Agrawala A. The horus location determination system. Wireless Networks. 2008 Jan;14:357-74. https://doi.org/10.1007/s11276-006-0725-7
  26. Jun M, Xuansong L, Xianping T, Jian L. Cluster filtered KNN: A WLAN-based indoor positioning scheme. 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks; 2008 Jun 23-26; Newport Beach, CA. New Jersey (US): Institute of Electrical and Electronics Engineers; 2008. p. 1-8. https://doi.org/10.1109/WOWMOM.2008.4594840
  27. Li B, Salter J, Dempster AG, Rizos C. Indoor positioning techniques based on wireless LAN. 1st IEEE International Confernce on Wireless Broadband and Ultra Wideband Communications; 2006 Mar 16; Sydney (Australia). New Jersey (US): Institute of Electrical and Electronics Engineers; 2006. p. 1-7.
  28. Bahl P, Padmanabhan VN. RADAR: An in-building RF-based user location and tracking system. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies; 2000 Mae 26-30; Tel Aviv, Israel. New Jersey (US): Institute of Electrical and Electronics Engineers; 2000. p. 775-84. https://doi.org/10.1109/INFCOM.2000.832252
  29. Li D, Zhang B, Li C. A feature-scaling-based k-nearest neighbor algorithm for indoor positioning systems. IEEE Internet of Things Journal. 2016 Aug;3(4):590-7. https://doi.org/10.1109/JIOT.2015.2495229
  30. Abdullah O, Abdel-Qader I. A PNN-Jensen-Bregman divergence symmetrization for a WLAN indoor positioning system. 2016 IEEE International Conference on Electro Information Technology; 2016 May 19-21; Grand Forks, ND. New Jersey (US): Institute of Electrical and Electronics Engineers; 2016. p. 362-7. https://doi.org/10.1109/EIT.2016.7535266
  31. Zhao H, Huang B, Jia B. Applying kriging interpolation for WiFi Fingerprinting based indoor positioning systems. 2016 IEEE Wireless Communications and Networking Conference; 2016 Apr 3-6; Doha, Qatar. New Jersey (US): Institute of Electrical and Electronics Engineers; 2016. p. 1-6. https://doi.org/10.1109/WCNC.2016.7565018
  32. Lemic F, Behboodi A, Handziski V, Wolisz A. Experimental decomposition of the performance of Fingerprinting-based localization algorithms. 2014 International Conference on Indoor Positioning and Indoor Navigation; 2014 Oct 27-30; Busan, Korea. New Jersey (US): Institute of Electrical and Electronics Engineers; 2014. p. 355-64. https://doi.org/10.1109/IPIN.2014.7275503
  33. Mingzhe X, Jiabin C, Chunlei S, Nan L, Kong C. The indoor positioning algorithm research based on improved location Fingerprinting. The 27th Chinese Control and Decision Conference; 2015 May 23-25; Qingdao, China. New Jersey (US): Institute of Electrical and Electronics Engineers; 2015. p. 5736-9. https://doi.org/10.1109/CCDC.2015.7161827
  34. Li N, Becerik-Gerber B, Krishnamachari B, Soibelman L. A BIM centered indoor localization algorithm to support building fire emergency response operations. Automation in Construction. 2014 Jun;42:78-89. https://doi.org/10.1016/j.autcon.2014.02.019
  35. Lee CJ, Yang HK. A system to detect potential fires using a thermographic camera. Natural Hazards. 2018 Feb;92:511-23. https://doi.org/10.1007/s11069-018-3224-0