Indoor Disaster Detection and Real-Time Escape Guidance System

  • Authors

    • Dae-Yeon Noh
    • Yoon-Young Park
    • Kang-Hee Jung
    • Ho-Won Lee
    • Joong-Eup Kye
    https://doi.org/10.14419/ijet.v7i3.24.22688

    Received date: December 1, 2018

    Accepted date: December 1, 2018

    Published date: April 25, 2026

  • Disaster, Indoor, Escape, Simulation, Detection.

  • Abstract

    Background/Objectives: When disasters occur in crowded indoor spaces, many property and human lives are lost. Simulate an indoor disaster escape system to minimize damage.

    Methods/Statistical analysis: Data measured by sensors or disaster robots are sent to middleware responsible for the area. Each middleware uses data to determine whether a disaster has occurred indoors. If a disaster occurs, locate victims of disaster and provide safe escape routes through pre-built maps. Through these methods, we simulate whether a disaster occurred and how well we can identify the escape route.

    Findings: BLE BEACON was used to locate victims of disaster. Later, he informed the victim of the shortest route to escape through middleware and servers. As a result, they were able to create indoor navigation with errors of about 3 meters. Using this system, it was confirmed that it could escape indoors.

    Improvements/Applications: The indoor disaster detection and escape route guidance system provides information on the existence of an indoor disaster. And the victim can identify safe escape routes.

  • References

    1. Hong YS. Constructing Status of Overseas Disaster Networks & Trends in Disaster Communication Technology and Standards. TTA Journal; 2010. p. 44-50.
    2. Kim SG, KIM TH, Tak SW. Performance Evaluation of RSSI-based Trilateraion Localization Methods. Journal of the Korea Institute of Information and Communication Engineering. 2011 Nov;15(11):2488-2492. Available from: http://www.dbpia.co.kr/Journal/ArticleDetail/NODE02245877
    3. Rida ME, Liu F, Jadi Y, Algawhari AAA, Askourih A. Indoor location position based on Bluetooth signal strength. ICISCE:IEEE; 2015 April. p. 769-773. DOI:10.1109/ICISCE.2015.177
    4. Chai S, An R, Du Z. An Indoor Positioning Algorithm Using Bluetooth Low Energy RSSI. International Conference on Advanced Material Science and Environmental Engineering. 2016.
    5. Alliance Z. Zigbee-2006 specification. 2006. Available from: http://www.zigbee.org/
    6. Alliance Z. IEEE 802.15.4. ZigBee standard. 2009.
    7. Sichitiu ML, Ramadurai V. Localization of wireless sensor networks with a mobile beacon. MASS; 2004 July. p. 174-183
    8. Thaljaoui A, Val T, Nasri N, Brulin D. BLE Localization using RSSI Measurements and iRingLA. ICIT:IEEE; 2015 march. p. 2178-2183. DOI:10.1109/ICIT.2015.7125418
    9. Nasipuri A, Li K. A directionality based location discovery scheme for wireless sensor networks. ACM. 2012. p. 105-111. DOI:10.1145/570738.570754
    10. Lorincz K, Welsh M. MoteTrack: A Robust, Decentralized Approach to RF-Based Location Tracking. International Symposium on Location-and Context-Awareness. 2005. p. 63-82
    11. Nallapaneni Manoj Kumar, Sonali Goel, Pradeep Kumar Mallick, “Smart Cities in India: Features, Policies, Current Status, and Challenges” , IEEE International Conference on Technologies for Smart-City Energy Security and Power (ICSESP 2018), C. V. Raman College of Engineering, Bhubaneswar, March 28-30, 2018., Electronic ISBN: 978-1-5386-4581-9, DOI: 10.1109/ICSESP.2018.8376669.

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  • How to Cite

    Noh, D.-Y., Park, Y.-Y., Jung, K.-H., Lee, H.-W., & Kye, J.-E. (2026). Indoor Disaster Detection and Real-Time Escape Guidance System. International Journal of Engineering and Technology, 7(3.24), 370-374. https://doi.org/10.14419/ijet.v7i3.24.22688