Advertisement

DNA Cryptography-Based Secured Weather Prediction Model in High-Performance Computing

  • Animesh Kairi
  • Suruchi Gagan
  • Tania Bera
  • Mohuya Chakraborty
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 811)

Abstract

This paper discusses the design of a DNA cryptography-based secured weather prediction model by the use of supercomputing or cluster type computing environment. The model is based on Markov’s chain. The supercomputer clusters are mainly required to run high-resource and time-demanding applications which a single computer cannot run. Use of supercomputers ensures faster and efficient computational power. High-performance computing (HPC) can be used to build a centralized file server for the Web and can easily process the information with its high processing speeds. A weather prediction system generally involves a large amount of past data to be processed over for an efficient prediction of the future weather. This paper lays emphasis on the use of Markov’s chain to develop a weather prediction model which depends on a larger set of input data types and can be implemented on a HPC system environment for a lesser computational time and higher accuracy. A flexible algorithm is proposed for weather prediction named averaged transit prediction (ATP) algorithm here. This model has been further integrated with a novel DNA cryptography-based algorithm named decimal bond DNA (DBD) algorithm for secured transmission of data between different processors of HPC. The simulated results on test bed formed by connecting five nodes in parallel mode forming supercomputing environment and having a performance of 0.1 Tflops gave predicted temperature, humidity, and wind speed for three different days with an accuracy of 85–95%.

Keywords

HPC Weather forecasting model Markov’s chain Weather prediction Cluster computing Numerical weather prediction DNA cryptography 

References

  1. 1.
  2. 2.
    Ateniese, G., Pietro, R.D., Mancini, L.V., Tsudik, G.: Scalable and efficient provable data possession. In: Proceedings of SecureComm’08, pp. 1–10 (2008)Google Scholar
  3. 3.
    Rittinghouse, J.: HPC: Implementation, Management, and Security. Amazon Book Stores (2009)Google Scholar
  4. 4.
    Miller, M.: HPC: web-based applications that change the way you work and collaborate online. Online Journal (August 2008 Issue)Google Scholar
  5. 5.
    Curtmola, R., Khan, O., Burns, R., Ateniese, G.: MRPDP: multiple-replica provable data possession. In: Proceedings of ICDCS’08, pp. 411–420 (2008)Google Scholar
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
    Gu, Y., Grossman, R.L.: Sector and sphere: the design and implementation of a high performance data cloud. UK (2008)Google Scholar
  12. 12.
    Beltrn-Castro, J., Valencia-Aguirre, J., Orozco-Alzate, M., Castellanos-Domnguez, G., Travieso-Gonzlez, C.M.: Rainfall forecasting based on ensemble empirical mode decomposition and neural networks. In: Rojas, I., Joya, G., Gabestany, J. (eds.) Advances in Computational Intelligence. Lecture Notes in Computer Science, vol. 7902, pp. 471–480. Springer Berlin, Heidelberg (2013)Google Scholar
  13. 13.
    Abhishek, K., Kumar, A., Ranjan, R., Kumar, S.: A rainfall prediction model using artificial neural network. In: 2012 IEEE Control and System Graduate Research Colloquium (ICSGRC), pp. 82–87, July 2012Google Scholar
  14. 14.
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.
    Hernandez, E., Sanchez-Anguix, V., Julian, V., Palanca, J., Duque, N.: Rainfall prediction: a deep learning approach. In: Conference paper, April 2016Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyInstitute of Engineering & ManagementSalt Lake, KolkataIndia

Personalised recommendations