Advertisement

Blockchain-Based Distributed Cooperative Control Algorithm for WSN Monitoring

  • Roberto Casado-VaraEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

The management of heterogeneous distributed sensor networks requires new solutions to address the problem of data quality and false data detection in Wireless Sensor Networks (WSN). In this paper, we present a nonlinear cooperative control algorithm based on game theory and blockchain. Here, a new model is proposed for the automatic processing and management of data in heterogeneous distributed wireless sensor networks stored in a blockchain. We apply our algorithm for improving temperature data quality in indoor surfaces.

Keywords

Wireless Sensors Network Blockchain Game theory Nonlinear models and systems Data quality False data detection Nonlinear cooperative control 

Notes

Acknowledgments

This paper has been funded by the European Regional Development Fund (FEDER) within the framework of the Interreg program V-A Spain-Portugal 2014-2020 (PocTep) grant agreement No 0123_IOTEC_3_E (project IOTEC).

References

  1. 1.
    Li, T., Sun, S., Bolic̀, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Signal Process. 119, 115–127 (2016).  https://doi.org/10.1016/j.sigpro.2015.07.013CrossRefGoogle Scholar
  2. 2.
    Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015).  https://doi.org/10.1016/j.amc.2015.08.059CrossRefGoogle Scholar
  3. 3.
    Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., De Las, M., Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis. BioMed Res. Int. 2015, 168682 (2015).  https://doi.org/10.1155/2015/168682CrossRefGoogle Scholar
  4. 4.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014 - 17th International Conference on Information Fusion (2014)Google Scholar
  5. 5.
    Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS ONE 9(7) (2014).  https://doi.org/10.1371/journal.pone.0102744CrossRefGoogle Scholar
  6. 6.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014 - 17th International Conference on Information Fusion (2014)Google Scholar
  7. 7.
    García Coria, J.A., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4 PART 1), 1189–1205 (2014).  https://doi.org/10.1016/j.eswa.2013.08.003CrossRefGoogle Scholar
  8. 8.
    Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for ambient intelligence systems. Inf. Sci. 222, 47–65 (2013).  https://doi.org/10.1016/j.ins.2011.05.002CrossRefGoogle Scholar
  9. 9.
    Costa, A., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic J. IGPL 20(4), 689–698 (2012).  https://doi.org/10.1093/jigpal/jzr021MathSciNetCrossRefGoogle Scholar
  10. 10.
    García, E., Rodriguez, S., Martin, B., Zato, C., Perez, B.: MISIA: middleware infrastructure to simulate intelligent agents. Advances in Intelligent and Soft Computing, vol. 91 (2011)Google Scholar
  11. 11.
    Rodriguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6077 LNAI (2010)Google Scholar
  12. 12.
    Rodriguez, S., Gil, O., De La Prieta, F., Zato, C., Corchado, J.M., Vega, P., Francisco, M.: People detection and stereoscopic analysis using MAS. In: INES 2010 - 14th International Conference on Intelligent Engineering Systems, Proceedings (2010).  https://doi.org/10.1109/INES.2010.5483855
  13. 13.
    Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010).  https://doi.org/10.1016/j.ins.2009.12.032CrossRefGoogle Scholar
  14. 14.
    Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for Alzheimer health care. Int. J. Ambient Comput. Intell. 1(1), 15–26 (2009).  https://doi.org/10.4018/jaci.2009010102CrossRefGoogle Scholar
  15. 15.
    Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Syst. Appl. 36(4), 8239–8246 (2009).  https://doi.org/10.1016/j.eswa.2008.10.003CrossRefGoogle Scholar
  16. 16.
    Glez-Peña, D., Diaz, F., Hernandez, J.M., Corchado, J.M., Fdez-Riverola, F.: geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinform. 10 (2009).  https://doi.org/10.1186/1471-2105-10-187CrossRefGoogle Scholar
  17. 17.
    Fernandez-Riverola, F., Diaz, F., Corchado, J.M.: Reducing the memory size of a fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(1), 138–146 (2007).  https://doi.org/10.1109/TSMCC.2006.876058CrossRefGoogle Scholar
  18. 18.
    Mendez, J.R., Fdez-Riverola, F., Diaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 4065, pp. 106–120 (2006)CrossRefGoogle Scholar
  19. 19.
    Mendez, J.R., Fdez-Riverola, F., Iglesias, E.L., Diaz, F., Corchado, J.M.: Tracking concept drift at feature selection stage in SpamHunting: an anti-spam instance-based reasoning system. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 4106, pp. 504–518 (2006)Google Scholar
  20. 20.
    Fdez-Rtverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004).  https://doi.org/10.1023/B:APIN.0000043558.52701.b1CrossRefGoogle Scholar
  21. 21.
    Corchado, J.M., Pavon, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3155, pp. 547–559 (2004).  https://doi.org/10.1007/978-3-540-28631-8
  22. 22.
    Laza, R., Pavn, R., Corchado, J.M.: A reasoning model for CBR-BDI agents using an adaptable fuzzy inference system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3040, pp. 96–106. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the Ocean’s CO2 budget with a CoHeL-IBR system. In: Advances in Case-Based Reasoning, Proceedings, vol. 3155, pp. 533–546 (2004)Google Scholar
  24. 24.
    Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10 (2004)Google Scholar
  25. 25.
    Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood hebbian learning based retrieval method for CBR systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2689, pp. 107–121 (2003)Google Scholar
  26. 26.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl. Based Syst. 16(5-6), 321–328 (2003).  https://doi.org/10.1016/S0950-7051(03)00034-0CrossRefGoogle Scholar
  27. 27.
    Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Int. J. Eng. Intell. Syst. Electr. Eng. Commun. 10(3), 173 (2002)Google Scholar
  28. 28.
    Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32(4), 307–313 (2002).  https://doi.org/10.1109/tsmcc.2002.806072CrossRefGoogle Scholar
  29. 29.
    Fyfe, C., Corchado, J.: A comparison of Kernel methods for instantiating case based reasoning systems. Adv. Eng. Inform. 16(3), 165–178 (2002).  https://doi.org/10.1016/S1474-0346(02)00008-3CrossRefGoogle Scholar
  30. 30.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001).  https://doi.org/10.1002/int.1024CrossRefzbMATHGoogle Scholar
  31. 31.
    Li, T.-C., Su, J.-Y., Liu, W., Corchado, J.M.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. Inf. Technol. Electron. Eng. 18(12), 1913–1939 (2017)CrossRefGoogle Scholar
  32. 32.
    Casado-Vara, R., Prieto-Castrillo, F., Corchado, J.M.: A game theory approach for cooperative control to improve data quality and false data detection in WSN. Int. J. Robust Nonlinear ControlGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BISITE Digital Innovation HubUniversity of Salamanca, Edificio Multiusos I+D+iSalamancaSpain

Personalised recommendations