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Intelligent IoT Monitoring System Using Rule-Based for Decision Supports in Fired Forest Images

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 379)

Abstract

Recently, many investigations focus on studying to detect of forest fires using IoT devices such as remote sensors or conventional fire detector sensors. However, supports in fire forest in real-time are hard for current studies in large forests. This paper has presented a novel approach to forest fire detection implemented using an improved rule-based integrated with k-means algorithm to improve the detection of forest fires. The rules in knowledge based can be considered in a camera as forest fires in real-time detection. The research explores the construction of Time-Lapse Videos from cluttered consecutive image. Mechanisms have been developed to automatically render the images with these elements from the scenes to produce more ‘truthful’ videos which more accurately describe of forest fires. The experimental results show that our proposed IoT monitoring system achieves significant improvements in ‘real-time’ fire detection.

Keywords

  • Video time lapse
  • Rule-based
  • Clustering
  • K-means
  • IoT fire forest system
  • Intelligent forest monitoring

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Notes

  1. 1.

    https://people.eecs.berkeley.edu/.

  2. 2.

    Earthengine: https://earthengine.google.com/timelapse/.

References

  1. Tiep, V.H.: The base of machine learning (2018)

    Google Scholar 

  2. Wang, Z., Jensen, J.R., Im, J.: An automatic region-based image segmentation algorithm for remote sensing applications. Environ. Modell. Softw. 25(10), 1149–1165 (2010)

    CrossRef  Google Scholar 

  3. González, J.C., Salazar, Ò.D.C.: Image enhancement with Matlab algorithms. Blekinge Institute of Technology Department of Applied Signal Processing SE-371 79, Karlskrona Sweden (2016)

    Google Scholar 

  4. Kajla, S., Bansal, R.: Efficient improved K means clustering for image segmentation. Int. J. Innov. Res. Comput. Commun. Eng. 4(6) (2016)

    Google Scholar 

  5. Bisla, A., Yadav, P.: Image segmentation using K-means clustering algorithm. Machine Learning Project (2019)

    Google Scholar 

  6. Rajkumar, S., Malathi, G.: A comparative analysis on image quality assessment for real time satellite images. Indian J. Sci. Technol. 9(34) (2016). School of Computing Science and Engineering, VIT University, Chennai, 600127, Tamil Nadu, India

    Google Scholar 

  7. Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. COM–28, 84–95 (1980)

    CrossRef  Google Scholar 

  8. Huang, C., Harris, R.: A comparison of several vector quantization codebook generation approaches. IEEE Trans. Image Process. 2(1), 108–112 (1993)

    CrossRef  Google Scholar 

  9. John, N., Viswanath, A., Sowmya, V., Soman, K.P.: Analysis of various color space models on effective single image super resolution, August 2016

    Google Scholar 

  10. Mathur, G., Purohit, H.: Performance analysis of color image segmentation using K- means clustering algorithm in different color spaces. IOSR J. VLSI Signal Process. (IOSR-JVSP) 4(6), 1–4 (2014). Ver. III

    Google Scholar 

  11. Aqil Burney, S.M., Tariq, H.: K-means cluster analysis for image segmentation. Int. J. Comput. Appl. (0975–8887) 96(4) (2014)

    Google Scholar 

  12. Vipin, V.: Image processing based forest fire detection. Int. J. Emerg. Technol. Adv. Eng. 2(2), 87–95 (2012)

    Google Scholar 

  13. Tuba, V., Capor-Hrosik, R., Tuba, E.: Forest fires detection in digital images based on color features. Int. J. Environ. Sci. 2 (2017)

    Google Scholar 

  14. Mehaboobsab, S.N., Alamgeer, A.K., Ahmed, M.M.N., Badruddin, A.M.S.: Fire detection system using image processing. Department of Electronics and Telecommunication Engineering Anjuman-I-Islam’s Kalsekar Technical Campus, 2015–2016

    Google Scholar 

  15. Binti Zaidi, N.I., binti Lokman, N.A.A., bin Daud, M.R., Achmad, H., Chia, K.A.: Fire recognition using RGB and YCbCr color space. ARPN J. Eng. Appl. Sci. 10(21), 9786–9790 (2015)

    Google Scholar 

  16. Garcia-Jimenez, S., Jurio, A., Pagola, M., De Miguel, L., Barrenechea, E., Bustince, H.: Forest fire detection: a fuzzy system approach based on overlap indices. Appl. Soft Comput. 52, 834–842 (2017)

    CrossRef  Google Scholar 

  17. Lin, H., Liu, X., Wang, X., Liu, Y.: A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks. Sustain. Comput.: Inf. Syst. 18, 101–111 (2018)

    Google Scholar 

  18. Zhang, Q., Lin, G., Zhang, Y., Gao, X., Wang, J.: Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. Proc. Eng. 211, 441–446 (2018)

    CrossRef  Google Scholar 

  19. Muhammad, K., Ahmad, J., Baik, S.W.: Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288(2), 30–42 (2018)

    CrossRef  Google Scholar 

  20. Pham, H.V., Moore, P., Tran, K.D.: Context matching with reasoning and decision support using hedge algebra with Kansei evaluation. In: Proceedings of the fifth symposium on Information and Communication Technology (SoICT 2014), Hanoi, Vietnam, 4–5 December 2014, pp. 202–210 (2014)

    Google Scholar 

  21. Moore, P., Pham, H.V.: Intelligent context with decision support under uncertainty. In: Second International Workshop on Intelligent Context-Aware Systems (ICAS 2012), pp. 977–982 (2012)

    Google Scholar 

  22. Van Pham, H., Moore, P.: Emergency service provision using a novel hybrid SOM-spiral STC model for group decision support under dynamic uncertainty. Appl. Sci. 9, 3910 (2019)

    CrossRef  Google Scholar 

  23. Image data sets in Berkely Univ. Libaray. https://people.eecs.berkeley.edu/yang/software/lossy_segmentation

  24. Image forest data sets. https://earthengine.google.com/timelapse

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Acknowledgment

This work was supported by the University of Economics Ho Chi Minh City under project CS-2020-15.

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Correspondence to Hai Van Pham .

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Van Pham, H., Nguyen, Q.H. (2021). Intelligent IoT Monitoring System Using Rule-Based for Decision Supports in Fired Forest Images. In: Vo, NS., Hoang, VP., Vien, QT. (eds) Industrial Networks and Intelligent Systems. INISCOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-77424-0_30

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  • DOI: https://doi.org/10.1007/978-3-030-77424-0_30

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