Fire detection by fusing correlated measurements

  • S. Hamed JavadiEmail author
  • Abdolreza Mohammadi
Original Research


Wireless sensor networks (WSNs) consist of smart nodes that observe a phenomenon of interest (POI) via several sensors. They are extensively used in environment surveillance and can be fit very well in fire detection where detecting fire correctly in real time while avoiding false alarms is crucial. Detection in each node is carried out by fusing the data of the sensors connected to that node. In this paper, a data fusion scheme is proposed in which the measurements of temperature and relative humidity sensors are fused while the correlation among them is resolved using the copula theory. The proposed scheme is validated using a practical data set.


Copula theory Correlation Data fusion Dependency Fire detection Internet of things Wireless sensor networks 


  1. Bhattacharjee S, Roy P, Ghosh S, Misra S, Obaidat MS (2012) Wireless sensor network-based fire detection, alarming, monitoring and prevention system for bord-and-pillar coal mines. J Syst Softw 85(3):571–581CrossRefGoogle Scholar
  2. Blum RS (1996a) Locally optimum distributed detection of correlated random signals based on ranks. IEEE Trans Inf Theory 42(3):931–942CrossRefzbMATHGoogle Scholar
  3. Blum RS (1996b) Necessary conditions for optimum distributed detectors under the Neyman–Pearson criterion. IEEE Trans Inf Theory 42(3):990–994CrossRefzbMATHGoogle Scholar
  4. Blum RS, Kassam SA (1992) Optimum distributed detection of weak signals in dependent sensors. IEEE Trans Inf Theory 38(3):1066–1079CrossRefzbMATHGoogle Scholar
  5. Chair Z, Varshney P (1986) Optimal data fusion in multiple sensor detection systems. IEEE Trans Aerosp Elect Syst AES 22(1):98–101CrossRefGoogle Scholar
  6. Cheong P, Chang KF, Lai YH, Ho SK, Sou IK, Tam KW (2011) A zigbee-based wireless sensor network node for ultraviolet detection of flame. IEEE Trans Ind Electron 58(11):5271–5277CrossRefGoogle Scholar
  7. Ciuonzo D, Salvo Rossi P (2014) Decision fusion with unknown sensor detection probability. IEEE Signal Process Lett 21(2):208–212CrossRefGoogle Scholar
  8. Ciuonzo D, Papa G, Romano G, Salvo Rossi P, Willett P (2013a) One-bit decentralized detection with a rao test for multisensor fusion. IEEE Signal Process Lett 20(9):861–864CrossRefGoogle Scholar
  9. Ciuonzo D, Romano G, Salvo Rossi P (2013b) Performance analysis and design of maximum ratio combining in channel-aware mimo decision fusion. IEEE Trans Wirel Commun 12(9):4716–4728CrossRefGoogle Scholar
  10. Ciuonzo D, De Maio A, Salvo Rossi P (2015) A systematic framework for composite hypothesis testing of independent bernoulli trials. IEEE Signal Process Lett 22(9):1249–1253CrossRefGoogle Scholar
  11. Drakopoulos E, Lee CC (1991) Optimum multisensor fusion of correlated local decisions. IEEE Trans Aerosp Elect Syst 27(4):593–606CrossRefGoogle Scholar
  12. Duffie JA, Beckman WA (2013) Solar engineering of thermal processes. Wiley, New YorkCrossRefGoogle Scholar
  13. Fang J, Li H (2009) Hyperplane-based vector quantization for distributed estimation in wireless sensor networks. IEEE Trans Inf Theory 55(12):5682–5699MathSciNetCrossRefzbMATHGoogle Scholar
  14. Ferrari G, Martalo M, Pagliari R (2011) Decentralized detection in clustered sensor networks. IEEE Trans Aerosp Elect Syst 47(2):959–973. doi: 10.1109/taes.2011.5751237 CrossRefGoogle Scholar
  15. Ferrari G, MartalÚ M, Abrardo A (2014) Information fusion in wireless sensor networks with source correlation. Inf Fusion 15:80–89CrossRefGoogle Scholar
  16. He H, Varshney PK (2015) Fusing censored dependent data for distributed detection. IEEE Trans Sign Proc 63(16):4385–4395MathSciNetCrossRefzbMATHGoogle Scholar
  17. Iyengar SG, Varshney PK, Damarla T (2011) A parametric copula-based framework for hypothesis testing using heterogeneous data. IEEE Trans Sign Proc 59(5):2308–2319MathSciNetCrossRefzbMATHGoogle Scholar
  18. Iyengar SG, Niu R, Varshney PK (2012) Fusing dependent decisions for hypothesis testing with heterogeneous sensors. IEEE Trans Sign Proc 60(9):4888–4897MathSciNetCrossRefzbMATHGoogle Scholar
  19. Javadi SH (2016a) Decision fusion: Sparse network vs. dense network. In: 24th Iranian Conf. Elect. Eng. (ICEE), pp 1821–1824Google Scholar
  20. Javadi SH (2016) Detection over sensor networks: a tutorial. IEEE Aerosp Elect Syst Mag 31(3):2–18. doi: 10.1109/MAES.2016.140128 CrossRefGoogle Scholar
  21. Javadi SH, Peiravi A (2012) Reliable distributed detection in multi-hop clustered wireless sensor networks. IET Signal Process 6(8):743–750CrossRefGoogle Scholar
  22. Javadi SH, Peiravi A (2015) Fusion of weighted decisions in wireless sensor networks. IET Wirel Sensor Syst 5(2):97–105CrossRefGoogle Scholar
  23. Karl H, Willig A (2005) Protocols and architectures for wireless sensor networks. Wiley, Chichester, West SussexCrossRefGoogle Scholar
  24. Katenka N, Levina E, Michailidis G (2008) Local vote decision fusion for target detection in wireless sensor networks. IEEE Trans Signal Process 56(1):329–338MathSciNetCrossRefzbMATHGoogle Scholar
  25. Kay SM (1998) Fundamentals of statistical signal processing, Volume 2: detection theory. Prentice Hall PTR, New JerseyGoogle Scholar
  26. Koutsopoulos I, Halkidi M (2014) Distributed energy-efficient estimation in spatially correlated wireless sensor networks. Comput Commun 45:47–58CrossRefGoogle Scholar
  27. Lloret J, Garcia M, Bri D, Sendra S (2009) A wireless sensor network deployment for rural and forest fire detection and verification. Sensors 9(11):8722–8747CrossRefGoogle Scholar
  28. Luo H, Liu Y, Das SK (2006) Routing correlated data with fusion cost in wireless sensor networks. IEEE Trans Mobile Comput 5(11):1620–1632CrossRefGoogle Scholar
  29. May A, Mitchell V, Piper J (2014) A user centred design evaluation of the potential benefits of advanced wireless sensor networks for fire-in-tunnel emergency response. Fire Saf J 63:79–88CrossRefGoogle Scholar
  30. Nelsen RB (2006) An introduction to copulas, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  31. Niu R (2005) Varshney PK (2005) Distributed detection and fusion in a large wireless sensor network of random size. EURASIP J Wirel Commun Netw 4:462–472zbMATHGoogle Scholar
  32. Niu R, Varshney PK, Cheng Q (2006) Distributed detection in a large wireless sensor network. Inf Fusion 7(4):380–394. doi:10.1016/j.inffus.2005.06.003, URL
  33. Noordin NH, Ney HW (2016) Localization in wireless sensor network for forest fire detection. In: 2016 IEEE 3rd International Symposium on Telecommunication Technologies (ISTT), pp 87–90Google Scholar
  34. Papoulis A, Pillai SU (2002) Probability, random variables and stochastic processes, 4th edn. McGraw-Hill, NYGoogle Scholar
  35. Rossia JL, Chetehounab K, Collinc A, Morettia B, Balbia JH (2010) Simplified flame models and prediction of the thermal radiation emitted by a flame front in an outdoor fire. Combust Sci Technol 182(10):1457–1477CrossRefGoogle Scholar
  36. Rybicki GB, Lightman AP (1979) Radiative processes in astrophysics. Wiley-Interscience, New YorkGoogle Scholar
  37. Schmidt T (2007) Coping with copulas. Risk Books, LondonGoogle Scholar
  38. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, LondonCrossRefzbMATHGoogle Scholar
  39. Son B, Her Y, Kim J (2006) A design and implementation of forest-fires surveillance system based on wireless sensor network for south korea mountains. Int J Comput Sci Netw Secur 6(9):124–130Google Scholar
  40. Sundaresan A, Varshney PK, Rao NSV (2007) Distributed detection of a nuclear radioactive source using fusion of correlated decisions. In: 10th Int. Conf. Inf. Fusion, pp 1–7Google Scholar
  41. Sundaresan A, Varshney PK, Rao NSV (2011) Copula-based fusion of correlated decisions. IEEE Trans Aerosp Elect Syst 47(1):454–471CrossRefGoogle Scholar
  42. Tenny RR, Sandell NR (1981) Detection with distributed sensors. IEEE Trans Signal Process AES 17(4):501–510MathSciNetGoogle Scholar
  43. Tsitsiklis JN (1993a) Decentralized detection. Adv Stat Signal Proces 2:297–344Google Scholar
  44. Tsitsiklis JN (1993b) Extremal properties of likelihood-ratio quantizers. IEEE Trans Commun 41(4):550–558MathSciNetCrossRefzbMATHGoogle Scholar
  45. Van Trees HL (2002) Detection of signals—estimation of signal parameters. Wiley, pp 239–422. URL
  46. Veeravalli VV, Varshney PK (2011) Distributed inference in wireless sensor networks. Phil Trans R Soc A: Math Phys Eng Sci 370(1958):100–117MathSciNetCrossRefzbMATHGoogle Scholar
  47. Vetterli M (2017) Sensorscope: Sensor networks for environmental monitoring. URL
  48. Vijayalakshmi S, Muruganand S (2016) Real time monitoring of wireless fire detection node. Procedia Technol 24:1113–1119CrossRefGoogle Scholar
  49. Viswanathan R, Varshney PK (1997) Distributed detection with multiple sensors: part I-fundamentals. Proc IEEE 85(1):54–63. doi: 10.1109/5.554208 CrossRefGoogle Scholar
  50. Willett P, Swaszek PF, Blum RS (2000) The good, bad, and ugly: distributed detection of a known signal in dependent gaussian noise. IEEE Trans Signal Process 48(12):3266–3279MathSciNetCrossRefGoogle Scholar
  51. Zervas E, Mpimpoudis A, Anagnostopoulos C, Sekkas O, Hadjiefthymiades S (2011) Multisensor data fusion for fire detection. Inf Fusion 12(3):150–159CrossRefGoogle Scholar
  52. Zhu Y, Vedantham R, Park SJ, Sivakumar R (2008) A scalable correlation aware aggregation strategy for wireless sensor networks. Inf Fusion 9(3):354–369CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.University of BojnordBojnordIran

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