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Decision Making Under Uncertainty for the Deployment of Future Networks in IoT Scenarios

Conference paper
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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 355)

Abstract

The main characteristic of various emerging communication network paradigms in the dimensioning, control and deployment of future networks is the fact that they are human-centric, entailing closely-knit interactions between telematics and human activities. Considering the effect of user behavior, whose dynamics are difficult to model, new uncertainties are introduced in these systems, bringing about network resource management challenges. Within this context, this study seeks to review different decision-making computational methods in conditions of uncertainty for Internet of Things scenarios such as smart spaces, and industry 4.0, through a systematic literature review. According to our research results, a new paradigm for computationally capturing and modeling human behavior context must be developed with the purpose of improving resource management.

Keywords

Uncertainty Resource management Decision making 

References

  1. 1.
    Almeida, A.T.d., Morais, D.C., Alencar, L.H., Clemente, T.R.N., Krym, E.M., Barboza, C.Z.: A multicriteria decision model for technology readiness assessment for energy based on PROMETHEE method with surrogate weights. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 64–68 (2014)Google Scholar
  2. 2.
    Asadabadi, M.R.: The stratified multi-criteria decision-making method. Knowl.-Based Syst. 162, 115–123 (2018)CrossRefGoogle Scholar
  3. 3.
    Ashton K.: That ‘Internet of Things’ thing in the real world, things matter more than ideas. RFID J. (2009) Google Scholar
  4. 4.
    Cables, E., Lamata, M., Verdegay, J.: RIM-reference ideal method in multicriteria decision making. Inf. Sci. 337–338, 1–10 (2016)CrossRefGoogle Scholar
  5. 5.
    Cables, E.H., Lamata, M.T., Verdegay, J.L.: Ideal reference method with linguistic labels: a comparison with LTOPSIS. In: Bello, R., Falcon, R., Verdegay, J.L. (eds.) Uncertainty Management with Fuzzy and Rough Sets. SFSC, vol. 377, pp. 115–126. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-10463-4_6CrossRefGoogle Scholar
  6. 6.
    Cearley, D., Burke, B., Furlonger, D., Kandaswamy, R., Litan, A.: Top 10 Strategic Technology Trends for 2019. Technical report, March 2019, Gartner (2019)Google Scholar
  7. 7.
    Chahuara, P., Portet, F., Vacher, M.: Context-aware decision making under uncertainty for voice-based control of smart home. Expert Syst. Appl. 75, 63–79 (2017)CrossRefGoogle Scholar
  8. 8.
    Chen, S.M., Cheng, S.H., Lan, T.C.: A new multicriteria decision making method based on the topsis method and similarity measures between intuitionistic fuzzy sets. In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 692–696. IEEE, July 2016Google Scholar
  9. 9.
    Conti, M., Passarella, A., Das, S.K.: The Internet of People (IoP): a new wave in pervasive mobile computing. Pervasive Mob. Comput. 41, 1–27 (2017)CrossRefGoogle Scholar
  10. 10.
    Dammak, F., Baccour, L., Ayed, A.B., Alimi, A.M.: ELECTRE method using interval-valued intuitionistic fuzzy sets and possibility theory for multi-criteria decision making problem resolution. In: IEEE International Conference on Fuzzy Systems, pp. 1–6. IEEE, July 2017Google Scholar
  11. 11.
    Dix, A.: Human-computer interaction, foundations and new paradigms. J. Vis. Lang. Comput. 42, 122–134 (2016)CrossRefGoogle Scholar
  12. 12.
    Fei, X., et al.: CPS data streams analytics based on machine learning for cloud and fog computing: a survey. Future Gen. Comput. Syst. 90, 435–450 (2019)CrossRefGoogle Scholar
  13. 13.
    Ferrara, M., Rasouli, S., Khademi, M., Salimi, M.: A robust optimization model for a decision-making problem: an application for stock market. Oper. Res. Perspect. 4, 136–141 (2017)MathSciNetGoogle Scholar
  14. 14.
    Fraile, F., Flores, J.L., Poler, R., Saiz, E.: Software-defined networking to improve cybersecurity in manufacturing oriented interoperability ecosystems. In: Popplewell, K., Thoben, K.-D., Knothe, T., Poler, R. (eds.) Enterprise Interoperability VIII. PIC, vol. 9, pp. 31–41. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-13693-2_3CrossRefGoogle Scholar
  15. 15.
    Gervasio, H., Da Silva, L.S.: A probabilistic decision-making approach for the sustainable assessment of infrastructures. Expert Syst. Appl. 39(8), 7121–7131 (2012)CrossRefGoogle Scholar
  16. 16.
    Jiang, W., Strufe, M., Schotten, H.D.: A SON decision-making framework for intelligent management in 5G mobile networks. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 1158–1162. IEEE, December 2017Google Scholar
  17. 17.
    Ken, S.A.F. et al.: RFID and the inclusive model for the Internet of Things (2009)Google Scholar
  18. 18.
    Khezrimotlagh, D., Chen, Y.: Data envelopment analysis. In: International Series in Operations Research and Management Science, vol. 269, pp. 217–234. Springer, Dordrecht (2018)Google Scholar
  19. 19.
    Kochenderfer, M.J., et al.: Decision Making Under Uncertainty: Theory and Application. MIT Lincoln Laboratory Series (2015)Google Scholar
  20. 20.
    Kreutz, D., Ramos, F.M.V., Veríssimo, P.E., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2015)CrossRefGoogle Scholar
  21. 21.
    Kumar, G.: A multi-criteria decision making approach for recommending a product using sentiment analysis. In: 2018 12th International Conference on Research Challenges in Information Science (RCIS), pp. 1–6. IEEE, May 2018Google Scholar
  22. 22.
    Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)CrossRefGoogle Scholar
  23. 23.
    Ma, Y.W., Chen, Y.C., Chen, J.L.: SDN-enabled network virtualization for industry 4.0 based on IoTs and cloud computing. In: 2017 19th International Conference on Advanced Communication Technology (ICACT), pp. 199–202. IEEE (2017)Google Scholar
  24. 24.
    Morente-Molinera, J.A., Kou, G., Samuylov, K., Ureña, R., Herrera-Viedma, E.: Carrying out consensual group decision making processes under social networks using sentiment analysis over comparative expressions. Knowl.-Based Syst. 165, 335–345 (2019)CrossRefGoogle Scholar
  25. 25.
    Mousavi, S.M., Gitinavard, H., Siadat, A.: A new hesitant fuzzy analytical hierarchy process method for decision-making problems under uncertainty. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 622–626 (2014)Google Scholar
  26. 26.
    Perçin, S.: Evaluating airline service quality using a combined fuzzy decision-making approach. J. Air Transp. Manag. 68, 48–60 (2018)CrossRefGoogle Scholar
  27. 27.
    Qin, J., Liu, X., Pedrycz, W.: An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment. Knowl.-Based Syst. 86, 116–130 (2015)CrossRefGoogle Scholar
  28. 28.
    Chen, S., Liu, J., Wang, H., Augusto, J.C.: An evidential reasoning based approach for decision making with partially ordered preference under uncertainty. In: 2013 International Conference on Machine Learning and Cybernetics, vol. 04, pp. 1712–1717. IEEE, July 2013Google Scholar
  29. 29.
    Smet, Y.D.: About the computation of robust PROMETHEE II rankings: empirical evidence. In: 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1116–1120 (2016)Google Scholar
  30. 30.
    Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016)CrossRefGoogle Scholar
  31. 31.
    Wan, J., et al.: Software-defined industrial Internet of Things in the context of industry 4.0. IEEE Sens. J. 16(20), 1–1 (2016)CrossRefGoogle Scholar
  32. 32.
    Wei, L., Yuan, Z., Yan, Y., Hou, J., Qin, T.: Evaluation of energy saving and emission reduction effect in thermal power plants based on entropy weight and PROMETHEE method. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 143–146 (2016)Google Scholar
  33. 33.
    Yu, B., Cai, M., Li, Q.: A \(\lambda \)-rough set model and its applications with TOPSIS method to decision making. Knowl.-Based Syst. 165, 420–431 (2019)CrossRefGoogle Scholar
  34. 34.
    Zeng, P., Wang, Z., Jia, Z., Kong, L., Li, D., Jin, X.: Time-slotted software-defined industrial ethernet for real-time quality of service in industry 4.0. Future Gen. Comput. Syst. 99, 1–10 (2019)CrossRefGoogle Scholar
  35. 35.
    Zhang, P., Yao, H., Qiu, C., Liu, Y.: Virtual network embedding using node multiple metrics based on simplified ELECTRE method. IEEE Access 6, 37314–37327 (2018)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2021

Authors and Affiliations

  1. 1.Cooperative University of ColombiaSantiago de CaliColombia
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.Escola Politécnica of the University of São PauloSão PauloBrazil

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