Intelligent Emergency Alarm System based on Multimedia IoT for Smart City

  • Kim, Shin (Department of Computer, Information & Communication Engineering, Konkuk University) ;
  • Yoon, Kyoungro (Department of Smart ICT Convergence, Konkuk University)
  • 투고 : 2019.09.20
  • 심사 : 2019.09.26
  • 발행 : 2019.09.30

초록

These-days technology related to IoT (Internet of Thing) is widely used and there are many types of smart system based IoT like smart health, smart building and so on. In smart health system, it is possible to check someone's health by analyzing data from wearable IoT device like smart watch. Smart building system aims to collect data from sensor such as humidity, temperature, human counter like that and control the building for energy efficiency, security, safety and so forth. Furthermore, smart city system can comprise several smart systems like smart building, smart health, smart mobility, smart energy and etc. In this paper, we propose multimedia IoT based intelligent emergency alarm system for smart city. In existing IoT based smart system, it communicates lightweight data like text data. In the past, due to network's limitations lightweight IoT protocol was proposed for communicating data between things but now network technology develops, problem which is to communicate heavy data is solving. The proposed system obtains video from IP cameras/CCTVs, analyses the video by exploiting AI algorithm for detecting emergencies and prevents them which cause damage or death. If emergency is detected, the proposed system sends warning message that emergency may occur to people or agencies. We built prototype of the intelligent emergency alarm system based on MQTT and assured that the system detected dangerous situation and sent alarm messages. From the test results, it is expected that the system can prevent damages of people, nature and save human life from emergency.

키워드

참고문헌

  1. Kim, Yi-Seul, and Jinsoo Cho. "The Telemedicine System based ECG Data using Bio-Signal Meter and Smart Device," Journal of the Semiconductor & Display Technology 11.3, pp. 51-56, 2012.
  2. Hahn, Jong-Woo, and Young-Kyu Choi. "A Real-time Vehicle Localization Algorithm for Autonomous Parking System," Journal of the Semiconductor & Display Technology 10.2, pp. 31-38, 2011.
  3. Chu, Yeon Ho, Bok Joo Lee, and Young Kyu Choi. "A Video based Traffic Light Recognition System for Intelligent Vehicles," Journal of the Semiconductor & Display Technology 14.2, pp. 29-34, 2015.
  4. Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-Khah, M., & Siano, P. "Iot-based smart cities: a survey," 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC). IEEE, pp.1-6, 2016.
  5. Dutta, J, and Sarbani R. "IoT-fog-cloud based architecture for smart city: Prototype of a smart building," 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence. IEEE, pp. 237-242, 2017.
  6. Gaur, A., Scotney, B., Parr, G., & McClean, S., "Smart city architecture and its applications based on IoT," Procedia computer science 52, pp. 1089-1094, 2015. https://doi.org/10.1016/j.procs.2015.05.122
  7. Nitti, M., Pilloni, V., Giusto, D., & Popescu, V., "Iot architecture for a sustainable tourism application in a smart city environment," Mobile Information Systems, 2017.
  8. Imteaj, A., Rahman, T., Hossain, M. K., Alam, M. S., & Rahat, S. A., "An IoT based fire alarming and authentication system for workhouse using Raspberry Pi 3," 2017 International conference on electrical, computer and communication engineering (ECCE). IEEE, pp. 899-904, 2017.
  9. Kang, D. H., Park, M. S., Kim, H. S., Kim, D. Y., Kim, S. H., Son, H. J., & Lee, S. G., "Room temperature control and fire alarm/suppression IoT service using MQTT on AWS," 2017 International Conference on Platform Technology and Service (PlatCon). IEEE, 2017.
  10. Ko, Byoung Chul, Kwang-Ho Cheong, and Jae-Yeal Nam, "Fire detection based on vision sensor and support vector machines," Fire Safety Journal 44.3, pp.322-329, 2009. https://doi.org/10.1016/j.firesaf.2008.07.006
  11. Yu, Chunyu, Zhibin Mei, and Xi Zhang. "A real-time video fire flame and smoke detection algorithm," Procedia Engineering 62, pp. 891-898, 2013. https://doi.org/10.1016/j.proeng.2013.08.140
  12. Wong, Arthur KK, and N. K. Fong, "Experimental study of video fire detection and its applications," Procedia engineering 71, pp. 316-327, 2014. https://doi.org/10.1016/j.proeng.2014.04.046
  13. OASIS Standard. 2014. MQTTversion3.1.1, http://docs.oasis-open.org/mqtt/mqtt/v3.1.1 (accessed Aug. 22, 2019).
  14. Chollet, F., "Xception: Deep learning with depthwise separable convolutions," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1251-1258, 2017.
  15. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z., "Rethinking the inception architecture for computer vision," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.
  16. Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement," arXiv preprint, 2018, arXiv:1804.02767.