Article metrics

  • citations in SCindeks: 0
  • citations in CrossRef:0
  • citations in Google Scholar:[=>]
  • visits in previous 30 days:0
  • full-text downloads in 30 days:0
article: 10 from 34  
Back back to result list
Facta universitatis - series: Working and Living Environmental Protection
2013, vol. 10, iss. 1, pp. 37-51
article language: English
document type: unclassified
published on: 23/01/2014
Mining and predicting rate-of-rise heat detector data
aUniversity of East Sarajevo, Faculty of Electrical Engineering, Republic of Srpska, B&H
bUniversity of Novi Sad, Faculty of Technical Science


Extreme events like fire can cause massive damage to indoor areas and life threatening conditions. Early residential fire detection is important for life preservation, prompt extinguishing and reducing damage. To detect fire, one or a combination of sensors (heat detectors, smoke detectors, flame detectors) and a detection algorithm are needed. The sensors might be a part of a wireless sensor network (WSN) or work independently. One of the most frequently used heat detectors is the rate-of-rise heat detector. In this paper some of the data mining algorithms on simulation data of the rate-of-rise heat detector are applied. Data mining seems to be an effective technique for discovering useful knowledge from a large amount of data observed by many sensors. Prediction in sensor networks can be performed in the way that each sensor learns a local predictive model for the global target classes, using only its local input data. Only the predicted target class for each reading is then transmitted to the gateway or to the base station. One important class of such algorithms are predictors, which use the sensor inputs to predict some output function of interest. The purpose of the paper is to analyze different classification algorithms in the case of rate-of-rise heat detector to see which of the applied techniques led to higher accuracy and fewer errors.


Data Mining; J48; Naïve Bayes; Neural Network; Rate-of-rise Heat detector; SVM


*** WEKA data mining tool. Available:
*** (1999) NFPA 72: National fire alarm code®
Aggarwal, C.C. (2013) Managing and Mining Sensor Data. New York: Springer
Antono, Ir.M.H. Automatic fire detector spacing: MSFPE introductions an appendix. i. e. of NFPA72, b. Appendix B
Bahrepour, M., Meratnia, N., Havinga, P.J.M. (2009) Use of AI techniques for residential fire detection in wireless sensor networks. in: AIAI-2009 Workshops Proceedings, Thessaloniki, Greece, 311-321
Beyler, C.L. (1984) A design method for flaming fire detection. Fire Technology, 20(4): 5-16
Heskestad, G., Delichatsios, M.A. (1977) Environments of fire detectors, Phase 1: Effect of fire size, ceiling height, and material, Vol. 2: Analysis. Gaithersburg, MD: National Bureau of Standards, NBS-GCR-77-95, str. 100
Natajaran, J. (2006) Simulation of sensor responses of advanced security systems. University of Texas at Arlington, PhD thesis, Master Thesis
Witten, I.H., Frank, E., Hall, M.A. (2011) Data mining - practical machine learning tools and techniques. Amsterdam: Morgan Kaufmann
Yabuki1, N., Yoshida, Y., Tsukamoto, S., Fukuda, T. (2011) Data storage and data mining of building monitoring data with context. in: International Symposium on Automation and Robotics in Construction (ISARC2011) (XXVIII), Seoul, Proceedings, str. 377-378
Yuen, W.W., Chow, W.K. (2005) A Monte Carlo Approach for the Layout Design of Thermal Fire Detection System. Fire Technology, 41(2): 93-104
Zukoski, E.E., Kubota, T., Cetegen, B. (1981) Entrainment in fire plumes. Fire Safety Journal, 3(3): 107-121