Skip to main content
SpringerLink
Log in

Fire detection by fusing correlated measurements

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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):571–581

    Article  Google Scholar 

  • Blum RS (1996a) Locally optimum distributed detection of correlated random signals based on ranks. IEEE Trans Inf Theory 42(3):931–942

    Article  MATH  Google Scholar 

  • Blum RS (1996b) Necessary conditions for optimum distributed detectors under the Neyman–Pearson criterion. IEEE Trans Inf Theory 42(3):990–994

    Article  MATH  Google Scholar 

  • Blum RS, Kassam SA (1992) Optimum distributed detection of weak signals in dependent sensors. IEEE Trans Inf Theory 38(3):1066–1079

    Article  MATH  Google Scholar 

  • Chair Z, Varshney P (1986) Optimal data fusion in multiple sensor detection systems. IEEE Trans Aerosp Elect Syst AES 22(1):98–101

    Article  Google Scholar 

  • 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):5271–5277

    Article  Google Scholar 

  • Ciuonzo D, Salvo Rossi P (2014) Decision fusion with unknown sensor detection probability. IEEE Signal Process Lett 21(2):208–212

    Article  Google Scholar 

  • 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):861–864

    Article  Google Scholar 

  • 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):4716–4728

    Article  Google Scholar 

  • 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):1249–1253

    Article  Google Scholar 

  • Drakopoulos E, Lee CC (1991) Optimum multisensor fusion of correlated local decisions. IEEE Trans Aerosp Elect Syst 27(4):593–606

    Article  Google Scholar 

  • Duffie JA, Beckman WA (2013) Solar engineering of thermal processes. Wiley, New York

    Book  Google Scholar 

  • Fang J, Li H (2009) Hyperplane-based vector quantization for distributed estimation in wireless sensor networks. IEEE Trans Inf Theory 55(12):5682–5699

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ferrari G, MartalÚ M, Abrardo A (2014) Information fusion in wireless sensor networks with source correlation. Inf Fusion 15:80–89

    Article  Google Scholar 

  • He H, Varshney PK (2015) Fusing censored dependent data for distributed detection. IEEE Trans Sign Proc 63(16):4385–4395

    Article  MathSciNet  MATH  Google Scholar 

  • Iyengar SG, Varshney PK, Damarla T (2011) A parametric copula-based framework for hypothesis testing using heterogeneous data. IEEE Trans Sign Proc 59(5):2308–2319

    Article  MathSciNet  MATH  Google Scholar 

  • Iyengar SG, Niu R, Varshney PK (2012) Fusing dependent decisions for hypothesis testing with heterogeneous sensors. IEEE Trans Sign Proc 60(9):4888–4897

    Article  MathSciNet  MATH  Google Scholar 

  • Javadi SH (2016a) Decision fusion: Sparse network vs. dense network. In: 24th Iranian Conf. Elect. Eng. (ICEE), pp 1821–1824

  • Javadi SH (2016) Detection over sensor networks: a tutorial. IEEE Aerosp Elect Syst Mag 31(3):2–18. doi:10.1109/MAES.2016.140128

    Article  Google Scholar 

  • Javadi SH, Peiravi A (2012) Reliable distributed detection in multi-hop clustered wireless sensor networks. IET Signal Process 6(8):743–750

    Article  Google Scholar 

  • Javadi SH, Peiravi A (2015) Fusion of weighted decisions in wireless sensor networks. IET Wirel Sensor Syst 5(2):97–105

    Article  Google Scholar 

  • Karl H, Willig A (2005) Protocols and architectures for wireless sensor networks. Wiley, Chichester, West Sussex

    Book  Google Scholar 

  • Katenka N, Levina E, Michailidis G (2008) Local vote decision fusion for target detection in wireless sensor networks. IEEE Trans Signal Process 56(1):329–338

    Article  MathSciNet  MATH  Google Scholar 

  • Kay SM (1998) Fundamentals of statistical signal processing, Volume 2: detection theory. Prentice Hall PTR, New Jersey

  • Koutsopoulos I, Halkidi M (2014) Distributed energy-efficient estimation in spatially correlated wireless sensor networks. Comput Commun 45:47–58

    Article  Google Scholar 

  • 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):8722–8747

    Article  Google Scholar 

  • Luo H, Liu Y, Das SK (2006) Routing correlated data with fusion cost in wireless sensor networks. IEEE Trans Mobile Comput 5(11):1620–1632

    Article  Google Scholar 

  • 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 63:79–88

    Article  Google Scholar 

  • Nelsen RB (2006) An introduction to copulas, 2nd edn. Springer, New York

    MATH  Google Scholar 

  • Niu R (2005) Varshney PK (2005) Distributed detection and fusion in a large wireless sensor network of random size. EURASIP J Wirel Commun Netw 4:462–472

    MATH  Google Scholar 

  • 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, URLhttp://www.sciencedirect.com/science/article/pii/S1566253505000710

  • 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 87–90

  • Papoulis A, Pillai SU (2002) Probability, random variables and stochastic processes, 4th edn. McGraw-Hill, NY

    Google Scholar 

  • 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):1457–1477

    Article  Google Scholar 

  • Rybicki GB, Lightman AP (1979) Radiative processes in astrophysics. Wiley-Interscience, New York

    Google Scholar 

  • Schmidt T (2007) Coping with copulas. Risk Books, London

    Google Scholar 

  • Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London

    Book  MATH  Google Scholar 

  • 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):124–130

    Google Scholar 

  • 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 1–7

  • Sundaresan A, Varshney PK, Rao NSV (2011) Copula-based fusion of correlated decisions. IEEE Trans Aerosp Elect Syst 47(1):454–471

    Article  Google Scholar 

  • Tenny RR, Sandell NR (1981) Detection with distributed sensors. IEEE Trans Signal Process AES 17(4):501–510

    MathSciNet  Google Scholar 

  • Tsitsiklis JN (1993a) Decentralized detection. Adv Stat Signal Proces 2:297–344

    Google Scholar 

  • Tsitsiklis JN (1993b) Extremal properties of likelihood-ratio quantizers. IEEE Trans Commun 41(4):550–558

    Article  MathSciNet  MATH  Google Scholar 

  • Van Trees HL (2002) Detection of signals—estimation of signal parameters. Wiley, pp 239–422. URLhttp://dx.doi.org/10.1002/0471221082.ch4

  • Veeravalli VV, Varshney PK (2011) Distributed inference in wireless sensor networks. Phil Trans R Soc A: Math Phys Eng Sci 370(1958):100–117

    Article  MathSciNet  MATH  Google Scholar 

  • Vetterli M (2017) Sensorscope: Sensor networks for environmental monitoring. URLhttp://lcav.epfl.ch/op/edit/sensorscope-en

  • Vijayalakshmi S, Muruganand S (2016) Real time monitoring of wireless fire detection node. Procedia Technol 24:1113–1119

    Article  Google Scholar 

  • Viswanathan R, Varshney PK (1997) Distributed detection with multiple sensors: part I-fundamentals. Proc IEEE 85(1):54–63. doi:10.1109/5.554208

    Article  Google Scholar 

  • 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):3266–3279

    Article  MathSciNet  Google Scholar 

  • Zervas E, Mpimpoudis A, Anagnostopoulos C, Sekkas O, Hadjiefthymiades S (2011) Multisensor data fusion for fire detection. Inf Fusion 12(3):150–159

    Article  Google Scholar 

  • Zhu Y, Vedantham R, Park SJ, Sivakumar R (2008) A scalable correlation aware aggregation strategy for wireless sensor networks. Inf Fusion 9(3):354–369

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Bojnord, Bojnord, Iran

    S. Hamed Javadi & Abdolreza Mohammadi

Authors
  1. S. Hamed Javadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdolreza Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Hamed Javadi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javadi, S.H., Mohammadi, A. Fire detection by fusing correlated measurements. J Ambient Intell Human Comput 10, 1443–1451 (2019). https://doi.org/10.1007/s12652-017-0584-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-017-0584-3

Keywords

Search

Navigation