Advertisement

Wireless Personal Communications

, Volume 105, Issue 4, pp 1171–1213 | Cite as

FSB-System: A Detection System for Fire, Suffocation, and Burn Based on Fuzzy Decision Making, MCDM, and RGB Model in Wireless Sensor Networks

  • Mohammad Samadi GharajehEmail author
Article
  • 85 Downloads

Abstract

Wireless sensor networks (WSNs) are composed of low-power, large-scale, low-cost sensor nodes to sense environmental conditions (e.g., temperature). Fire is one of the most common hazards in the world so that detection of the fires can prevent a lot of damages to the lives. Fire detection process can be improved by using knowledge-based systems such as fuzzy decision making and multi-criteria decision making (MCDM). This paper proposes a detection system, called FSB-System, to predict the fire, suffocation, and burn probabilities over areas using fuzzy theory, MCDM, and an RGB model. The system uses sensing data of the temperature, smoke, and light sensors to determine appropriate, assorted decisions under different conditions. Three fuzzy controllers are suggested in FSB-System: fire fuzzy controller (namely FFC), suffocation fuzzy controller (namely SFC), and burn fuzzy controller (namely BFC). FFC determines the fire probability, SFC measures the suffocation probability, and BFC calculates the burn probability. Sensor nodes are randomly scattered over areas in a way that they form multiple clusters. Non-cluster heads (NCHs) transmit their sensing data to cluster heads (CHs). Furthermore, CHs transmit the gathered data to the native sink to report environmental conditions toward a base station (e.g., a fire department). The number of sinks is determined by a suggested MCDM controller based on network size and the number of clusters. Simulation results demonstrate that the proposed system surpasses the threshold methods in terms of remaining energy, the number of alive nodes, network lifetime, the number of wrong alerts, and financial losses. This system can be applied in various environments including forests, buildings, etc.

Keywords

Fire detection Wireless sensor networks (WSNs) Fuzzy decision making Multi-criteria decision making (MCDM) RGB model 

Notes

Supplementary material

11277_2019_6141_MOESM1_ESM.mp4 (16.6 mb)
Supplementary material 1 (MP4 17045 kb)
11277_2019_6141_MOESM2_ESM.mp4 (14.2 mb)
Supplementary material 2 (MP4 14581 kb)
11277_2019_6141_MOESM3_ESM.mp4 (14.3 mb)
Supplementary material 3 (MP4 14684 kb)

References

  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  2. 2.
    Cheraghlou, M. N., Babaie, S., & Samadi, M. (2012). LRC: Novel fault tolerant local re-clustering protocol for wireless sensor network. Journal of Computing, 4(8), 99–104.Google Scholar
  3. 3.
    Gharajeh, M. S., & Khanmohammadi, S. (2013). Static three-dimensional fuzzy routing based on the receiving probability in wireless sensor networks. Computers, 2(4), 152–175.CrossRefGoogle Scholar
  4. 4.
    Gharajeh, M. S. (2014). Determining the state of the sensor nodes based on fuzzy theory in WSNs. International Journal of Computers Communications & Control, 9(4), 419–429.CrossRefGoogle Scholar
  5. 5.
    Peng, S., Wang, T., & Low, C. P. (2015). Energy neutral clustering for energy harvesting wireless sensors networks. Ad Hoc Networks, 28, 1–16.CrossRefGoogle Scholar
  6. 6.
    Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: A survey. ACM Computing Surveys (CSUR), 38(4), 1–45.CrossRefGoogle Scholar
  7. 7.
    Kafi, M. A., Challal, Y., Djenouri, D., Doudou, M., Bouabdallah, A., & Badache, N. (2013). A study of wireless sensor networks for urban traffic monitoring: Applications and architectures. Procedia Computer Science, 19, 617–626.CrossRefGoogle Scholar
  8. 8.
    Shih, E. I., Shoeb, A. H., & Guttag, J. V. (2009). Sensor selection for energy-efficient ambulatory medical monitoring. In Proceedings of the 7th international conference on mobile systems, applications, and services, 2009, New York (pp. 347–358).Google Scholar
  9. 9.
    Keally, M., Zhou, G., & Xing, G. (2010). Watchdog: Confident event detection in heterogeneous sensor networks. In: IEEE 16th real-time and embedded technology and applications symposium (RTAS), Stockholm, April 12–15, 2010 (pp. 279–288).Google Scholar
  10. 10.
    Lin, K. (2013). Research on adaptive target tracking in vehicle sensor networks. Journal of Network and Computer Applications, 36(5), 1316–1323.CrossRefGoogle Scholar
  11. 11.
    Olivares, A., Olivares, G., Mula, F., Górriz, J. M., & Ramírez, J. (2011). Wagyromag: Wireless sensor network for monitoring and processing human body movement in healthcare applications. Journal of Systems Architecture, 57(10), 905–915.CrossRefGoogle Scholar
  12. 12.
    He, T., Krishnamurthy, S., Luo, L., Yan, T., Gu, L., Stoleru, R., et al. (2006). VigilNet: An integrated sensor network system for energy-efficient surveillance. ACM Transactions on Sensor Networks (TOSN), 2(1), 1–38.CrossRefGoogle Scholar
  13. 13.
    Wenshen, J., Ligang, P., Yuange, Q., Jihua, W., & Wenfu, W. (2011). Agro-food farmland environmental monitoring techniques and equipment. Procedia Environmental Sciences, 10, 2247–2255.CrossRefGoogle Scholar
  14. 14.
    Bonvoisin, J., Lelah, A., Mathieux, F., & Brissaud, D. (2012). An environmental assessment method for wireless sensor networks. Journal of Cleaner Production, 33, 145–154.CrossRefGoogle Scholar
  15. 15.
    Othman, M. F., & Shazali, K. (2012). Wireless sensor network applications: A study in environment monitoring system. Procedia Engineering, 41, 1204–1210.CrossRefGoogle Scholar
  16. 16.
    Sahoo, P. K., Sheu, J. P., & Hsieh, K. Y. (2013). Target tracking and boundary node selection algorithms of wireless sensor networks for internet services. Information Sciences, 230, 21–38.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Bottero, M., Chiara, B. D., & Deflorio, F. P. (2013). Wireless sensor networks for traffic monitoring in a logistic centre. Transportation Research Part C: Emerging Technologies, 26, 99–124.CrossRefGoogle Scholar
  18. 18.
    Vaidehi, V., Vardhini, M., Yogeshwaran, H., Inbasagar, G., Bhargavi, R., & Hemalathac, C. S. (2013). Agent based health monitoring of elderly people in indoor environments using wireless sensor networks. Procedia Computer Science, 19, 64–71.CrossRefGoogle Scholar
  19. 19.
    Janssens, A., Necheva, C., Tanner, V., & Turai, I. (2013). The new basic safety standards directive and its implications for environmental monitoring. Journal of Environmental Radioactivity, 125, 99–104.CrossRefGoogle Scholar
  20. 20.
    Felemban, E., Lee, C. G., & Ekici, E. (2006). MMSPEED: multipath multi-SPEED protocol for QoS guarantee of reliability and timeliness in wireless sensor networks. IEEE Transactions on Mobile Computing, 5(6), 738–754.CrossRefGoogle Scholar
  21. 21.
    Flammini, A., Ferrari, P., Marioli, D., Sisinni, E., & Taroni, A. (2009). Wired and wireless sensor networks for industrial applications. Microelectronics Journal, 40(9), 1322–1336.CrossRefGoogle Scholar
  22. 22.
    Kirchner, P., Oberländer, J., Friedrich, P., Berger, J., Rysstad, G., Keusgen, M., et al. (2012). Realization of a calorimetric gas sensor on polyimide foil for applications in aseptic food industry. Sensors and Actuators B: Chemical, 170, 60–66.CrossRefGoogle Scholar
  23. 23.
    Zhang, K. (2012). Design of real time monitor system of manufacture process of iron and steel industry based on new style sensors. Energy Procedia, 16, 627–632.CrossRefGoogle Scholar
  24. 24.
    Nauman, Z., Iqbal, S., Khan, M. I., & Tahir, M. (2011). WSN-based fire detection and escape system with multi-modal feedback. In: Multimedia communications, services and security (pp. 251–260).Google Scholar
  25. 25.
    Bouabdellah, K., Noureddine, H., & Larbi, S. (2013). Using wireless sensor networks for reliable forest fires detection. Procedia Computer Science, 19, 794–801.CrossRefGoogle Scholar
  26. 26.
    Pande, V., Elmannai, W., & Elleithy, K. (2013). Classification and detection of fire on WSN using IMB400 multimedia sensor board. In: IEEE Long Island systems, applications and technology conference (LISAT), Farmingdale, NY, May 3–3, 2013 (pp. 1–6).Google Scholar
  27. 27.
    Mao, J., Jannotti, J., Akdere, M., & Cetintemel, U. (2008). Event-based constraints for sensornet programming. In Proceedings of the second international conference on distributed event-based systems, New York, 2008 (pp. 103–113).Google Scholar
  28. 28.
    Deligiannakis, A., & Kotidis, Y. (2011). Detecting proximity events in sensor networks. Information Systems, 36(7), 1044–1063.CrossRefGoogle Scholar
  29. 29.
    Fawzy, A., Mokhtar, H. M. O., & Hegazy, O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian Informatics Journal, 14(2), 157–164.CrossRefGoogle Scholar
  30. 30.
    Vu, C. T., Beyah, R. A., & Yingshu, L. (2007). Composite event detection in wireless sensor networks. IEEE International Performance, Computing, and Communications Conference, New Orleans, LA, 11–13, 264–271.Google Scholar
  31. 31.
    Yun, M., Bragg, D., Arora, A., & Choi, H. A. (2011). Battle event detection using sensor networks and distributed query processing. In IEEE conference on computer communications workshops (INFOCOM WKSHPS), Shanghai, April 10–15, 2011 (pp. 750–755).Google Scholar
  32. 32.
    Wittenburg, G., Dziengel, N., Adler, S., Kasmi, Z., Ziegert, M., & Schiller, J. (2012). Cooperative event detection in wireless sensor networks. IEEE Communications Magazine, 50(12), 124–131.CrossRefGoogle Scholar
  33. 33.
    Cornell Database Group-Cougar, 2010. http://www.cs.cornell.edu/bigreddata/cougar/. Accessed 25 Jan 2018.
  34. 34.
    Govindan, R., Hellerstein, J., Hong, W., Madden, S., Franklin, M., & Shenker, S. (2002). The sensor network as a database. Technical Report 02-771, Computer Science Department, University of Southern California, 2002.Google Scholar
  35. 35.
    Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2003). The design of an acquisitional query processor for sensor networks. In Proceedings of the 2003 ACM SIGMOD international conference on management of data, USA, New York, 2003 (pp. 491–502).Google Scholar
  36. 36.
    Li, S., Son, S. H., & Stankovic, J. A. (2003). Event detection services using data service middleware in distributed sensor networks. In Information processing in sensor networks (pp. 502–517).Google Scholar
  37. 37.
    Sayakkara, A., Goonetillake, M., & Zoysa, K. D. (2012). Declarative interface for in-network actuation on wireless sensor-actuator networks. In IEEE 3rd international conference on networked embedded systems for every application (NESEA), Liverpool, December 13–14, 2012 (pp. 1–8).Google Scholar
  38. 38.
    Jiao, B., Son, S., & Stankovic, J. (2005). GEM: Generic event service middleware for wireless sensor networks. In INSS, USA.Google Scholar
  39. 39.
    Kapitanova, K., & Son, S. H. (2009). MEDAL: A compact event description and analysis language for wireless sensor networks. In Sixth international conference on networked sensing systems (INSS) (pp. 1–4).Google Scholar
  40. 40.
    Osterlind, F., Pramsten, E., Roberthson, D., Eriksson, J., Finne, N., & Voigt, T. (2007). Integrating building automation systems and wireless sensor networks. IEEE Conference on Emerging Technologies and Factory Automation, Patras, 25–28, 1376–1379.Google Scholar
  41. 41.
    Díaz-Ramírez, A., Tafoya, L. A., Atempa, J. A., & Mejía-Alvarezb, P. (2012). Wireless sensor networks and fusion information methods for forest fire detection. Procedia Technology, 3, 69–79.CrossRefGoogle Scholar
  42. 42.
    Yu, L., Wang, N., & Meng, X. (2005). Real-time forest fire detection with wireless sensor networks. International Conference on Wireless Communications, Networking and Mobile Computing, 2, 1214–1217.Google Scholar
  43. 43.
    Zhang, J., Li, W., Yin, Z., Liu, S., & Guo, X. (2009). Forest fire detection system based on wireless sensor network. In: 4th IEEE conference on industrial electronics and applications, Xi’an, May 25–27, 2009 (pp. 520–523).Google Scholar
  44. 44.
    Hartung, C., Han, R., Seielstad, C., & Holbrook, S. (2006). FireWxNet: A multi-tiered portable wireless system for monitoring weather conditions in wildland fire environments. In Proceedings of the 4th international conference on mobile systems, applications and services (pp. 28–41).Google Scholar
  45. 45.
    Song, W. S., & Hong, S. H. (2007). A reference model of fire detection and monitoring system using BACnet. Building and Environment, 42(2), 1000–1010.CrossRefGoogle Scholar
  46. 46.
    Chen, T. H., Wu, P. H., & Chiou, Y. C. (2004). An early fire-detection method based on image processing. In International conference on image processing (ICIP), Singapore, October 24–27, 2004 (Vol. 3, pp. 1707–1710).Google Scholar
  47. 47.
    Joseph, J. V. M., Pandurangam, M., & Somasekharan, M. (2007). Fire detection system: A device for document preservation in a library environment: Guidance for selection to installation of an ideal system. In Information Science & Technology, Kalpakkam, Tamil Nadu, 2007 (pp. 73–80).Google Scholar
  48. 48.
    Blagojevich, M., Petkovich, D., & Simich, D. (2001). A new algorithm for adaptive alarm threshold in fire detection system. NIST Special Publication SP, National Institute of Standards & Technology, 2001 (pp. 201–209).Google Scholar
  49. 49.
    Milke, J. A., & McAvoy, T. J. (1995). Analysis of signature patterns for discriminating fire detection with multiple sensors. Fire Technology, 31(2), 120–136.CrossRefGoogle Scholar
  50. 50.
    Gottuk, D. T., Peatross, M. J., Roby, R. J., & Beyler, C. L. (2002). Advanced fire detection using multi-signature alarm algorithms. Fire Safety Journal, 37(4), 381–394.CrossRefGoogle Scholar
  51. 51.
    Rose-Pehrsson, S. L., Hart, S., Street, T., Tatem, P., Williams, F., Hammond, M., Gottuk, D., Wright, M., & Wong, J. (2001). Real-time probabilistic neural network performance and optimization for fire detection and nuisance alarm rejection. NIST Special Publication SP, National Institute of Standards & Technology, 2001 (pp. 176–190).Google Scholar
  52. 52.
    Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.MathSciNetCrossRefzbMATHGoogle Scholar
  53. 53.
    Bolourchi, P., & Uysal, S. (2013). Forest fire detection in wireless sensor network using fuzzy logic. In Fifth international conference on computational intelligence, communication systems and networks (CICSyN), Madrid, June 5–7, 2013 (pp. 83–87).Google Scholar
  54. 54.
    Taheri, H., Neamatollahi, P., Younis, O. M., Naghibzadeh, S., & Yaghmaee, M. H. (2012). An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 10(7), 1469–1481.CrossRefGoogle Scholar
  55. 55.
    Rajesh, D. H., & Paramasivan, B. (2012). Fuzzy logic based performance optimization with data aggregation in wireless sensor networks. Procedia Engineering, 38, 3331–3336.CrossRefGoogle Scholar
  56. 56.
    Liang, Q., & Wang, L. (2005). Event detection in wireless sensor networks using fuzzy logic system. In Proceedings of the IEEE international conference on computational intelligence for homeland security and personal safety (CIHSPS), 2005 (pp. 52–55).Google Scholar
  57. 57.
    Marin-Perianu, M., & Havinga, P. (2007). D-FLER—A distributed fuzzy logic engine for rule-based wireless sensor networks. In Ubiquitous computing systems (pp. 86–101).Google Scholar
  58. 58.
    Novák, V. (2012). Reasoning about mathematical fuzzy logic and its future. Fuzzy Sets and Systems, 192, 25–44.MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Silveira, G. P., & de Barros, L. C. (2013). Numerical methods integrated with fuzzy logic and stochastic method for solving PDEs: An application to dengue. Fuzzy Sets and Systems, 225, 39–57.MathSciNetCrossRefzbMATHGoogle Scholar
  60. 60.
    Sadiq, R., Husain, T., Veitch, B., & Bose, N. (2004). Risk-based decision-making for drilling waste discharges using a fuzzy synthetic evaluation technique. Ocean Engineering, 31(16), 1929–1953.CrossRefGoogle Scholar
  61. 61.
    Duch, W., Adamczak, R., & Grabczewski, K. (2001). A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks, 12(2), 277–306.CrossRefGoogle Scholar
  62. 62.
    Passino, K. M., Yurkovich, S., & Reinfrank, M. (1998). Fuzzy control (Vol. 42). Reading: Addison-Wesley.Google Scholar
  63. 63.
    Pedrycz, W. (1994). Why triangular membership functions? Fuzzy Sets and Systems, 64(1), 21–30.MathSciNetCrossRefGoogle Scholar
  64. 64.
    Zhao, J., & Bose, B. K. (2002). Evaluation of membership functions for fuzzy logic controlled induction motor drive. In IEEE 28th annual conference of the industrial electronics society (IECON), November 5–8, 2002 (Vol. 1, pp. 229–234).Google Scholar
  65. 65.
    Botzheim, J., Hámori, B., & Kóczy, L. T. (2001). Extracting trapezoidal membership functions of a fuzzy rule system by bacterial algorithm. Computational Intelligence. Theory and Applications, 2206, 218–227.CrossRefzbMATHGoogle Scholar
  66. 66.
    Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.CrossRefzbMATHGoogle Scholar
  67. 67.
    Ross, T. J. (2004). Fuzzy logic with engineering applications (2nd ed.). New York: Wiley.zbMATHGoogle Scholar
  68. 68.
    Baležentis, T., & Baležentis, A. (2014). A survey on development and applications of the multi-criteria decision making method MULTIMOORA. Journal of Multi-Criteria Decision Analysis, 21(3–4), 209–222.CrossRefGoogle Scholar
  69. 69.
    Ramya, C. M., Shanmugaraj, M., & Prabakaran, R. (2011). Study on ZigBee technology. In IEEE 3rd international conference on electronics computer technology (ICECT), Kanyakumari, 2011 (Vol. 6, pp. 297–301).Google Scholar
  70. 70.
    Zhao, Q., Wu, K., Wu, J., & Wu, X. (2008). Design of physiological parameter acquisition and communication module based on CC2430. In Springer 7th Asian-Pacific Conference on Medical and Biological Engineering, 2008 (pp. 348–351).Google Scholar
  71. 71.
    De Silva, C. W. (2011). Zadeh–Macfarlane–Jamshidi theorems on decoupling of a fuzzy rule base. Scientia Iranica, 18(3), 611–616.CrossRefzbMATHGoogle Scholar
  72. 72.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on System sciences, January 4–7, 2000 (Vol. 2, pp. 1–10).Google Scholar
  73. 73.
    Zhao, F., Xu, Y., & Li, R. (2012). Improved LEACH routing communication protocol for a wireless sensor network. International Journal of Distributed Sensor Networks.  https://doi.org/10.1155/2012/649609.Google Scholar
  74. 74.
    Mahapatro, A., & Khilar, P. M. (2013). Fault diagnosis in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(4), 2000–2026.CrossRefGoogle Scholar
  75. 75.
    Gharajeh, M. S., & Hassanzadeh, R. (2017). Improving the fault tolerance of wireless sensor networks by a weighted criteria matrix. The Mediterranean Journal of Electronics and Communications, 13(1), 1–6.Google Scholar
  76. 76.
    Annual report 2012/2013 of Fire Department City of New York. (2017). http://www.nyc.gov/html/fdny/pdf/publications/annual_reports/2012_annual_report.pdf. Accessed 25 Jan 2018.
  77. 77.
    Gharajeh, M. S., & Khanmohammadi, S. (2015). Dispatching rescue and support teams to events using ad hoc networks and fuzzy decision making in rescue applications. Journal of Control and Systems Engineering, 3(1), 35–50.CrossRefGoogle Scholar
  78. 78.
    Gharajeh, M. S., & Khanmohammadi, S. (2016). DFRTP: Dynamic 3D fuzzy routing based on traffic probability in wireless sensor networks. IET Wireless Sensor Systems, 6(6), 211–219.CrossRefGoogle Scholar
  79. 79.
    Khanmohammadi, S., & Gharajeh, M. S. (2017). A routing protocol for data transferring in wireless sensor networks using predictive fuzzy inference system and neural node. Ad Hoc & Sensor Wireless Networks, 38(1–4), 103–124.Google Scholar
  80. 80.
    Gharajeh, M. S., & Alizadeh, M. (2016). OPCA: Optimized prioritized congestion avoidance and control for wireless body sensor networks. International Journal of Sensors, Wireless Communications and Control, 6(2), 118–128.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Young Researchers and Elite Club, Tabriz BranchIslamic Azad UniversityTabrizIran

Personalised recommendations