Hybridized Model for Early Detection and Smart Monitoring of Forest Fire

Authors

  • Mohammed Anas El abbassi Electrical Engineering Research Laboratory ENSET-Rabat, Mohammed V University in Rabat Rabat, Morocco
  • Abdelilah Jilbab Electrical Engineering Research Laboratory ENSET-Rabat, Mohammed V University in Rabat Rabat, Morocco
  • Abdennaser Bourouhou Electrical Engineering Research Laboratory ENSET-Rabat, Mohammed V University in Rabat Rabat, Morocco

DOI:

https://doi.org/10.14738/tmlai.54.3206

Keywords:

Wireless sensor networks (WSN), early detection, data fusion, scanning technique, fire event, monitoring.

Abstract

 The demand for wireless sensor network technology has been increasingly needed in recent years for several major applications, including environmental monitoring, where nodes deployed in nature detect, process and transfer the environmental data in an autonomous way. However, the performance of early detection of the fire event after data fusion processed by this WSN’s system will be less reliable since the majority of the other nodes do not detect the fire yet (at the beginning of event). That is why the present work is conducted. It proposes an intelligent strategy of data fusion of temperature and humidity sensors hybridized with an intelligent scanning technique. This model will allow the early detection of alarm from the beginning of fire event and guarantee a good monitoring of area state with an ongoing localization of fire zone. The result proves a very good performance in terms of reliability of the early detection and tracking fire propagation.

 

References

(1) K. Sohraby, D. Minoli, T. Znati .” Wireless sensor networks: technology, protocols, and applications”, John Wiley & Sons (2007).

(2) M. Hefeeda,M. Bagheri,”Forest Fire Modeling and Early Detection using Wireless Sensor Networks”, Ad Hoc & Sensor Wireless Networks Vol. 7, pp. 169–224,2009.

(3) E. Sisinni, A. Depari, A. Flammini,” Design and implementation of a wireless sensor network for temperature sensing in hostile environments”, Sensors and Actuators A: Physical, vol.237, pp. 47-55, 1 January 2016.

(4) E. Zervas , A. Mpimpoudis, C. Anagnostopoulos , O. Sekkas, and S. Hadjiefthymiades,”Multisensor data fusion for fire detection”, Information Fusion ,vol.12 :150–159, 2011.

(5) M .A.El abbassi, A.Jilbab,A.Bourouhou,”A robust model of multi-sensor data fusion applied in wireless sensor networks for fire detection”,International Review on Modelling and Simulation (IREMOS), vol.9, N°3,pp.173-180, Vol. 9, N. 3,pp 173-180, June

(6) Massart, D.L. “Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-nearest neighbour classification by using alternative voting rules.” Anal. Chim.Acta

, 15-27, January 1982.

(7) Vijay Kotu, Bala Deshpande, “Predictive Analytics and Data Mining,” ,chapitre 4, Pages 63-163 , 2015.

(8) R. Niu ,B. Chen, P.K. Varshney,” Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks “,IEEE Transactions on signal processing,Vol.54,NO.3,March 2006.

(9) Teknomo, Kardi(2015), “Similarity Measurement” http://people.revoledu.com/kardi/tutorial/Similarity/MinkowskiDistance.html ,(2015).

(10) M .A.El abbassi, A.Jilbab,A.Bourouhou, ” Detection model based on multi-sensor data for early fire prevention ”, IEEE ICEIT tangier Morocco, pp.214 – 218. 2016.

(11) B. Khaleghi, A. Khamis, F.O. Karray, S.N. Razavi, “Multisensor data fusion :A review of state-of-the-art, Information fusion”, vol 14: 28-44,2013.

(12) M.A.El abbassi, A.Jilbab, A.Bourouhou,” Fusion des données dans les réseaux de capteurs sans fils pour la protection de l’environnement “, TELECOM’2015 & 9ème JFMMA, Meknès- Maroc, 2015.

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Published

2017-09-01

How to Cite

El abbassi, M. A., Jilbab, A., & Bourouhou, A. (2017). Hybridized Model for Early Detection and Smart Monitoring of Forest Fire. Transactions on Engineering and Computing Sciences, 5(4). https://doi.org/10.14738/tmlai.54.3206

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems