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An indoor gas leakage source localization algorithm using distributed maximum likelihood estimation in sensor networks

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Abstract

Gas leakage source localization based on sensor networks has an important practical significance in many fields such as environmental monitoring, security protection and pollution control. This paper proposed a gas leakage source localization algorithm using distributed maximum likelihood estimation method for mobile sensor network to improve the lower performance with static sensor network. Firstly, the likelihood function of gas leakage source parameters was deduced based on the gas turbulent diffusion model. Then, the parameters of gas leakage source were estimated based on the likelihood function with the gas concentration measurement in environment. Finally, the gas leakage source location would be achieved through the iterative optimization of the likelihood function. The preliminary experimental results show that the proposed distributed Maximum Likelihood Estimation method could be achieved an acutely gas leakage source location in an indoor environment. And the reasonable path planning and dynamic topology changing could improve the positioning performance.

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References

  • Chraim F, Erol YB, Pister K (2016) Wireless gas leak detection and localization. IEEE Trans Ind Inf 12(2):768–779

    Article  Google Scholar 

  • Feng S, Wu C, Zhang Y et al (2014) Grid-based improved maximum likelihood estimation for dynamic localization of mobile robots. Int J Distrib Sens Netw 10(3):271547

    Article  Google Scholar 

  • Hosseini B, Stockie JM (2016a) Airborne contaminant source estimation using a finite-volume forward solver coupled with a Bayesian inversion approach

  • Hosseini B, Stockie JM (2016b) Bayesian estimation of airborne fugitive emissions using a Gaussian plume model. Atmos Environ 141:122–138

    Article  Google Scholar 

  • Hutchinson M, Oh H, Chen WH (2017) A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Inf Fusion 36:130–148

    Article  Google Scholar 

  • Keats A, Yee E, Lien FS (2007) Bayesian inference for source determination with applications to a complex urban environment. Atmos Environ 41(3):465–479

    Article  Google Scholar 

  • Keats A, Yee E, Lien FS (2010) Information-driven receptor placement for contaminant source determination. Environ Model Softw 25(9):1000–1013

    Article  Google Scholar 

  • Kuang X, Shao H (2006) Application of sensor networks in plume source position estimation. J East China Univ Sci Technol (Natural Science Edition) 32(7):780–783

    Google Scholar 

  • Li JG, Meng QH, Wang Y et al (2011) Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm. Auton Robots 30(3):281–292

    Article  Google Scholar 

  • Li H, Yang Y, Qiu X et al (2017) Gravitation-based 3-D redeployment schemes for the mobile sensors and sink in gas leakage monitoring. IEEE Access 5:8545–8558

    Article  Google Scholar 

  • Michaelides MP, Panayiotou CG (2009) Fault tolerant maximum likelihood event localization in sensor networks using binary data. IEEE Signal Process Lett 16(5):406–409

    Article  MATH  Google Scholar 

  • Michalis PM, Christos GP (2005) Plume source position estimation using sensor network. In: Proceedings of the 13th Mediterranean conference on control and automation, pp 731–736

  • Mitra S, Duttagupta SP, Tuckley DK et al (2012) 3D ad-hoc sensor networks based localization and risk assessment of buried landfill gas source. Int J Circ Syst Signal Process 6(1):75–86

    Google Scholar 

  • Özkan E, Lindsten F, Fritsche C et al (2015) Recursive maximum likelihood identification of jump Markov nonlinear systems. IEEE Trans Signal Process 63(3):754–765

    Article  MathSciNet  MATH  Google Scholar 

  • Ristic B, Morelande MR, Gunatilaka A (2010) Information driven search for point sources of gamma radiation. Sig Process 90(4):1225–1239

    Article  MATH  Google Scholar 

  • Ruhi H, Charles J, Ray L et al (2012) Atmospheric tomography: a Bayesian inversion technique for determining the rate and location of fugitive emissions. Environ Sci Technol 46(3):1739–1746

    Article  Google Scholar 

  • Senocak I, Hengartner NW, Short MB et al (2008) Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference. Atmos Environ 42(33):7718–7727

    Article  Google Scholar 

  • Shu L, Mukherjee M, Xu X et al (2016) A survey on gas leakage source detection and boundary tracking with wireless sensor networks. IEEE Access 4:1700–1715

    Article  Google Scholar 

  • Simonetto A, Leus G (2014) Distributed maximum likelihood sensor network localization. IEEE Trans Signal Processing 62(6):1424–1437

    Article  MathSciNet  MATH  Google Scholar 

  • Ushiku H, Ishida H et al (2006) Estimation of gas-source location using gas sensors and ultrasonic anemometer. In: The proceedings of IEEE sensors, pp 420–423

  • Vankayalapati N, Kay S, Ding Q (2014) TDOA based direct positioning maximum likelihood estimator and the Cramer-Rao bound. IEEE Trans Aerosp Electron Syst 50(3):1616–1635

    Article  Google Scholar 

  • Vijayakumaran S, Levinbook Y, Wong TF (2007) Maximum likelihood localization of a diffusive point source using binary observations. IEEE Trans Signal Process 55(2):665–676

    Article  MathSciNet  MATH  Google Scholar 

  • Wade D, Senocak I (2013) “Stochastic reconstruction of multiple source atmospheric contaminant dispersion events”. Atmos Environ 74(74):45–51

    Article  Google Scholar 

  • Wang Y, Huang H, Huang L et al (2017) Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: a comparison of likelihood functions and distance measures. Atmos Environ 152:519–530

    Article  Google Scholar 

  • Weimer J, Bruce HK, Mitchel J et al (2012) An approach to leak detection using wireless sensor networks at carbon sequestration sites. Int J Greenhouse Gas Control 9(7):243–253

    Article  Google Scholar 

  • Wu MW, Qinghao ZM (2015) Gas source localization based on mobile sensor network. J Tianjin Univ 2:139–146

    Google Scholar 

  • Xu J, He J, Zhang Y et al (2016) A distance-based maximum likelihood estimation method for sensor localization in wireless sensor networks. Int J Distrib Sens Netw 12(4):2080536

    Article  Google Scholar 

  • Zhao T, Nehorai A (2007) Distributed sequential Bayesian estimation of a diffusive source in wireless sensor networks. IEEE Trans Signal Process 55(4):2213–2225

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors wish to thank for the financial support of Natural Science Foundation of China (61573253, 61271321), Tianjin Natural Science Foundation (16JCYBJC16400), Tianjin Science and Technology Project (16YFZCGX00360,16ZXZNGX00080), Tianjin Science and Technology Project of Special Correspondent (17JCTPJC), National Training Programs of Innovation and Entrepreneurship for Undergraduates (201610069007, 201710069023). The corresponding author is Professor Zhang Liyi.

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Authors and Affiliations

  1. The College of Information, Tianjin University of Commerce, Tianjin, 300134, China

    Zhang Yong, Zhang Liyi, Han Jianfeng, Ban Zhe & Yang Yi

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  1. Zhang Yong
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  2. Zhang Liyi
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  3. Han Jianfeng
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  4. Ban Zhe
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  5. Yang Yi
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Correspondence to Zhang Liyi.

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Yong, Z., Liyi, Z., Jianfeng, H. et al. An indoor gas leakage source localization algorithm using distributed maximum likelihood estimation in sensor networks. J Ambient Intell Human Comput 10, 1703–1712 (2019). https://doi.org/10.1007/s12652-017-0624-z

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  • DOI: https://doi.org/10.1007/s12652-017-0624-z

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