An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF)

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Md Iftekharul Mobin, Md Abid-Ar-Rafi, Md Neamul Islam, Md Rifat Hasan

Md Iftekharul Mobin, Md Abid-Ar-Rafi, Md Neamul Islam and Md Rifat Hasan. Article: An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF). International Journal of Computer Applications 133(6):1-7, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Md Iftekharul Mobin and Md Abid-Ar-Rafi and Md Neamul Islam and Md Rifat Hasan},
	title = {Article: An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF)},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {6},
	pages = {1-7},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Safe From Fire (SFF) is an intelligent self controlled smart fire extinguisher system assembled with multiple sensors, actuators and operated by micro-controller unit (MCU). It takes input signals from various sensors placed in different position of the monitored area, and combines integrated fuzzy logic to identify fire breakout locations and severity. Data fusion algorithm facilitates the system to discard deceptive fire situations such as: cigarette smoke, welding etc. During the fire hazard SFF notifies the fire service and others by text messages and telephone calls. Along with ringing fire alarm it announces the fire affected locations and severity. To prevent fire from spreading it breaks electric circuits of the affected area, releases the extinguishing gas pointing to the exact fire locations. This paper presents how this system is built, components, and connection diagram and implementation logic. Overall performance is evaluated through experimental tests by creating real time fire hazard prototype scenarios to investigate reliability. It is observed that SFF system demonstrated its efficiency most of the cases perfectly.


  1. J. San-Miguel-Ayanz and N. Ravail, “Active fire detection for fire emergency management: Potential and limitations for the operational use of remote sensing,” Natural Hazards, vol. 35, no. 3, pp. 361–376, 2005.
  2. Z. Liu and A. K. Kim, “Review of recent developments in fire detection technologies,” Journal of Fire Protection Engineering, vol. 13, no. 2, pp. 129–151, 2003.
  3. T. Celik, H. Demirel, H. Ozkaramanli, and M. Uyguroglu, “Fire detection using statistical color model in video sequences,” Journal of Visual Communication and Image Representation, vol. 18, no. 2, pp. 176–185, 2007. Fig. 9: Alarm logs are shown in serial monitor of Arduino
  4. Y. Dedeoglu, B. U. Töreyin, U. Güdükbay, and A. E. Cetin, “Real-time fire and flame detection in video.” in ICASSP (2), 2005, pp. 669–672.
  5. A. Somov, D. Spirjakin, M. Ivanov, I. Khromushin, R. Passerone, A. Baranov, and A. Savkin, “Combustible gases and early fire detection: an autonomous system for wireless sensor networks,” in Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking. ACM, 2010, pp. 85–93.
  6. D. Krstini´c, D. Stipani?cev, and T. Jakov?cevi´c, “Histogrambased smoke segmentation in forest fire detection system,” Information Technology and Control, vol. 38, no. 3, 2015.
  7. C.-B. Liu and N. Ahuja, “Vision based fire detection,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 4. IEEE, 2004, pp. 134– 137.
  8. B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, “Wavelet based real-time smoke detection in video,” in Signal Processing Conference, 2005 13th European. IEEE, 2005, pp. 1–4.
  9. T. Çelik, H. Ozkaramanli, and H. Demirel, “Fire and smoke detection without sensors: image processing based approach,” in 15th European signal processing conference, EUSIPCO, 2007, pp. 147–158.
  10. K. Angayarkkani and N. Radhakrishnan, “Efficient forest fire detection system: a spatial data mining and image processing based approach,” International Journal of Computer Science and Network Security, vol. 9, no. 3, pp. 100–107, 2009.
  11. M. Bahrepour, N. Meratnia, and P. J. Havinga, “Automatic fire detection: A survey from wireless sensor network perspective,” 2008.
  12. A. Ollero, J. Martinez-De Dios, and B. Arrúe, “Integrated systems for early forest-fire detection,” in III International Conference on Forest Fire Research 14th Conference on Fire and Forest Meteorology, Luso, vol. 16, 1998, p. 20.
  13. L. Yu, N.Wang, and X. Meng, “Real-time forest fire detection with wireless sensor networks,” in Wireless Communications, Networking and Mobile Computing, 2005. Proceedings. 2005 International Conference on, vol. 2. IEEE, 2005, pp. 1214– 1217.
  14. T. L. Chien, K. L. Su, and J. H. Guo, “Develop a multi interface based detection module for home automation,” in The 1nd International Conference on New Technological Innovation for Position, 2004, pp. 289–294.
  15. B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, “Multisensor data fusion: A review of the state-of-the-art,” Information Fusion, vol. 14, no. 1, pp. 28–44, 2013.
  16. Mykh, “Burglar and fire alarm system arduino,” https://, 2013.
  17. C. Stanton, “Getting to know arduino : Part 1 : Hello, world!” 2014/03/28/getting-to-know-arduino-part-1-hello-world, 2014.
  18. M. E-commerce, “Arduino flame sensor digital sensor,” http: // html, 2015.
  19. P. Marian, “Sen-1327 lpg gas sensor module,” sen-1327-lpg-gas-sensor-module/, Feb 2015.
  20. S. C. Wiki, “Grove temperature and humidity sensor,” http: //, 2015.
  21. G. Wiki, “Arduino gprs shield,” wiki/index.php/Arduino_GPRS_Shield, June 2014.
  22. B. K. Bose, “Expert system, fuzzy logic, and neural network applications in power electronics and motion control,” Proceedings of the IEEE, vol. 82, no. 8, pp. 1303–1323, 1994.
  23. D. Izadi, J. H. Abawajy, S. Ghanavati, and T. Herawan, “A data fusion method in wireless sensor networks,” Sensors, vol. 15, no. 2, pp. 2964–2979, 2015.
  24. C. Elmas, O. Ustun, and H. H. Sayan, “A neuro-fuzzy controller for speed control of a permanent magnet synchronous motor drive,” Expert Systems with Applications, vol. 34, no. 1, pp. 657–664, 2008.
  25. K. Devi, R. Singh, S. Gautam, and D. Nagaria, “Speed control of induction motor using fuzzy logic approach,” 2015.
  26. W. Wang, B. Jiang, and L. Yang, “Design on fire alarm system based on bp neural network and multiple source information fusion,” in 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14). Atlantis Press, 2014.
  27. X. Le, “Fire Detection Robot using Type-2 Fuzzy Logic Sensor Fusion,” 2015.
  28. N. Cheng and Q.Wu, “A decision-making method for fire detection data fusion based on bayesian approach,” in Digital Manufacturing and Automation (ICDMA), 2013 Fourth International Conference on. IEEE, 2013, pp. 21–23.
  29. A. Díaz-Ramírez, L. A. Tafoya, J. A. Atempa, and P. Mejía- Alvarez, “Wireless sensor networks and fusion information methods for forest fire detection,” Procedia Technology, vol. 3, pp. 69–79, 2012.


Fire, sensors, fuzzy logic, data fusion, MCU, intelligent system, expert system