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Different InterCriteria Analysis of Variants of ACO algorithm for Wireless Sensor Network Positioning

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 838))

Abstract

Wireless sensor networks are formed by spatially distributed sensors, which communicate in a wireless way. This network can monitor various kinds of environment and physical conditions like movement, noise, light, humidity, images, chemical substances etc. A given area needs to be fully covered with minimal number of sensors and the energy consumption of the network needs to be minimal too. We propose several algorithms, based on Ant Colony Optimization, to solve the problem. We study the algorithms behavior when the number of ants varies from 1 to 10. We apply InterCriteria analysis to study relations between proposed algorithms and number of ants and analyse correlation between them. Four different algorithms of ICrA—\(\mu \)-biased, Balanced, \(\nu \)-biased and Unbiased—are applied. The obtained results are discussed in order to find the stronger correlations between considered hybrid ACO algorithms.

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References

  1. Atanassov, K.: Generalized index matrices. Comptes rendus de l’Academie Bulgare des Sciences 40(11), 15–18 (1987)

    MathSciNet  MATH  Google Scholar 

  2. Atanassov, K.: On index matrices, part 1: standard cases. Adv. Stud. Contemp. Math. 20(2), 291–302 (2010)

    MathSciNet  MATH  Google Scholar 

  3. Atanassov, K.: On index matrices, part 2: intuitionistic fuzzy case. Proc. Jangjeon Math. Soci. 13(2), 121–126 (2010)

    MathSciNet  MATH  Google Scholar 

  4. Atanassov, K.: Index Matrices: Towards an Augmented Matrix Calculus, Studies in Computational Intelligence, vol. 573 (2014)

    Google Scholar 

  5. Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, Berlin (2012)

    Chapter  Google Scholar 

  6. Atanassov, K., Vassilev, P.: On the intuitionistic fuzzy sets of n-th type. In: Gaweda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds.) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol. 738, pp. 265–274. Springer, Cham (2018)

    Google Scholar 

  7. Atanassov, K.: Intuitionistic Fuzzy Sets, VII ITKR Session, Sofia, 20–23 June 1983. Reprinted: Int. J. Bioautomation 20(S1), S1–S6 (2016)

    Article  MathSciNet  Google Scholar 

  8. Atanassov, K.: Review and New Results on Intuitionistic Fuzzy Sets, Mathematical Foundations of Artificial Intelligence Seminar, Sofia, (1988), Preprint IM-MFAIS-1-88. Reprinted: Int. J. Bioautomation, 20(S1), S7–S16 (2016)

    Google Scholar 

  9. Atanassov, K., Atanassova, V., Gluhchev, G.: InterCriteria analysis: ideas and problems. Notes on Intuitionistic Fuzzy Sets 21(2), 81–88 (2015)

    MATH  Google Scholar 

  10. Atanassov, K., Mavrov, D., Atanassova, V.: Intercriteria decision making: a new approach for multicriteria decision making. Based Index Matrices Intuitionistic Fuzzy Sets, Issues in IFSs and GNs 11, 1–8 (2014)

    MATH  Google Scholar 

  11. Atanassov, K., Szmidt, E., Kacprzyk, J.: On intuitionistic fuzzy pairs. Notes on Intuitionistic Fuzzy Sets 19(3), 1–13 (2013)

    Article  Google Scholar 

  12. Fidanova, S., Marinov, P., Alba, E.: Ant algorithm for optimal sensor deployment, computational intelligence. In: Madani, K., Correia, A.D., Rosa, A., Filipe, J. (eds.) Studies of Computational Intelligence, vol. 399, pp. 21–29 Springer (2012)

    Chapter  Google Scholar 

  13. Fidanova, S., Marinov, P., Paprzycki, M.: Influence of the number of ants on multi-objective ant colony optimization algorithm for wireless sensor network layout. In: Assessment of the Air Quality in Bulgaria-Short Summary Based on Recent Modelling Results, pp. 232–239 (2014). https://doi.org/10.1007/978-3-662-43880-0_25.

    Chapter  Google Scholar 

  14. Fidanova, S., Marinov, P.: Influence of the number of ants on mono-objective ant colony optimization algorithm for wireless sensor network layout. In: Proceedings of BGSIAM’12, pp. 59–66, Sofia, Bulgaria (2012)

    Google Scholar 

  15. Fidanova, S., Shindarov, M., Marinov, P.: Wireless sensor positioning using ACO algorithm. In: Sgurev, V., Yager, R., Kacprzyk, J., Atanassov, K. (eds.) Recent Contributions in Intelligent Systems, Studies in Computational Intelligence, Vol. 657, pp. 33–44. Springer, Cham (2017)

    Google Scholar 

  16. Fidanova, S., Roeva, O., Paprzycki, M., Gepner, P.: InterCriteria analysis of ACO start strategies. In: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, pp. 547–550 (2016)

    Google Scholar 

  17. Hernandez, H., Blum, C.: Minimum energy broadcasting in wireless sensor networks: an ant colony optimization approach for a realistic antenna model. J. Appl. Soft Comput. 11(8), 5684–5694 (2011)

    Article  Google Scholar 

  18. Ikonomov, N., Vassilev, P., Roeva, O.: ICrAData—software for intercriteria analysis. Int J Bioautomation 22(1), 1–10 (2018)

    Article  Google Scholar 

  19. D.B. Jourdan, Wireless Sensor Network Planning with Application to UWB Localization in GPS-denied Environments, Massachusets Institute of Technology, Ph.D. thesis (2000)

    Google Scholar 

  20. Konstantinidis, A., Yang, K., Zhang, Q., Zainalipour-Yazti, D.: A multiobjective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. J. Comput. Netw. 54(6), 960–976 (2010)

    Article  Google Scholar 

  21. Krawczak, M., Bureva, V., Sotirova, E., Szmidt, E.: Application of the InterCriteria decision making method to universities ranking. Adv. Intell. Syst. Comput. 401, 365–372 (2016)

    Google Scholar 

  22. Molina, G., Alba, E., Talbi, E.-G.: Optimal sensor network layout using multi-objective metaheuristics. Univers. Comput. Sci. 14(15), pp. 2549–2565 (2008)

    Google Scholar 

  23. Marinov, E., Vassilev, P., Atanassov, K.: On separability of intuitionistic fuzzy sets, In: Novel Developments in Uncertainty Representation and Processing, Advances in Intelligent Systems and Computing, Vol. 401, pp. 111–123. Springer, Cham (2106)

    Google Scholar 

  24. Ribagin, S., Shannon, A., Atanassov, K.: Intuitionistic fuzzy evaluations of the elbow joint range of motion. Adv. Intell. Syst. Comput. 401, 225–230 (2016)

    Google Scholar 

  25. Roeva, O., Vassilev, P., Ikonomov, N., Angelova, M., Su, J., Pencheva, T.: On Different Algorithms for InterCriteria Relations Calculation, Studies in Computational Intelligence, Vol. 757, pp. 143–160. Springer, Cham (2019)

    Google Scholar 

  26. Pottie, G.J., Kaiser, W.J.: Embedding the internet: wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)

    Article  Google Scholar 

  27. Todinova, S., Mavrov, D., Krumova, S., Marinov, P., Atanassova, V., Atanassov, K., Taneva, S.G.: Blood plasma thermograms dataset analysis by means of InterCriteria and correlation analyses for the case of colorectal cancer. Int. J. Bioautomation 20(1), 115–124 (2016)

    Google Scholar 

  28. Traneva, V., Atanassova V., Tranev S.: Index matrices as a decision-making tool for job appointment. In: Nikolov, G. et al. (Ed.) Springer Nature Switzerland AG, pp. 1–9. NMA 2018, LNCS 11189 (2019)

    Chapter  Google Scholar 

  29. Traneva, V., Tranev, S., Atanassova, V.: An Intuitionistic fuzzy approach to the hungarian algorithm. In: Nikolov, G. et al. (Ed.) Springer Nature Switzerland AG, pp. 1-9, NMA 2018, LNCS 11189, (2019)

    Chapter  Google Scholar 

  30. Vassilev, P.: A Note on New Distances between Intuitionistic Fuzzy Sets, Notes on Intuitionistic Fuzzy Sets, Vol. 21, No. 5, 11–15 (2015)

    Google Scholar 

  31. Vassilev, P., Todorova, L., Andonov, V.: An auxiliary technique for InterCriteria Analysis via a three dimensional index matrix, Notes on Intuitionistic Fuzzy Sets, vol. 21, No. 2, 71–76 (2015)

    Google Scholar 

  32. Vassilev, P., Ribagin, S.: A note on intuitionistic fuzzy modal-like operators generated by power mean. In: Kacprzyk J., Szmidt E., Zadrony S., Atanassov K., Krawczak M. (eds.) Advances in Fuzzy Logic and Technology 2017 (2018). EUSFLAT 2017, IWIFSGN, : Advances in Intelligent Systems and Computing, vol. 643, pp. 470–475. Springer, Cham (2017)

    Google Scholar 

  33. Wolf, S., Mezz, P.: Evolutionary local search for the minimum energy broadcast problem. In: Cotta, C., van Hemezl, J. (eds.) VOCOP 2008. Lecture Notes in Computer Sciences, vol. 4972, pp. 61–72. Springer, Germany (2008)

    Chapter  Google Scholar 

  34. Xu, Y., Heidemann, J., Estrin, D.: Geography informed energy conservation for Ad Hoc routing. In: Proceedings of the 7th ACM/IEEE Annual International Conference on Mobile Computing and Networking, Italy, pp. 70–84 (2001)

    Google Scholar 

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Acknowledgements

Work presented here is partially supported by the Bulgarian National Scientific Fund under Grants DFNI DN 12/5 “Efficient Stochastic Methods and Algorithms for Large-Scale Problems” and KP-06-N22/1 “Theoretical Research and Applications of InterCriteria Analysis”. The development of the proposed hybrid ACO algorithms has been funded by Grant DFNI DN 12/5. The study of influence of number of ants on ACO algorithm behaviour based on ICrA approach has been funded by Grant KP-06-N22/1.

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Correspondence to Stefka Fidanova .

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Roeva, O., Fidanova, S. (2020). Different InterCriteria Analysis of Variants of ACO algorithm for Wireless Sensor Network Positioning. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-030-22723-4_6

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