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Whale Optimization Algorithm with Exploratory Move for Wireless Sensor Networks Localization

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Hybrid Intelligent Systems (HIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1179))

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Abstract

In the modern era, with the development of new technologies, such as cloud computing and the internet of things, there is a greater focus on wireless distributed sensors, distributed data processing and remote operations. Low price and miniaturization of sensor nodes have led to a large number of applications, such as military, forest fire detection, remote surveillance, volcano monitoring, etc. The localization problem is among the greatest challenges in the area of wireless sensor networks, as routing and energy efficiency depend heavily on the positions of the nodes. By performing a survey of computer science literature, it can be observed that in the wireless sensor networks localization domain, swarm intelligence metaheuristics have generated compelling results. In the research proposed in this paper, a modified/improved whale optimization swarm intelligence algorithm, that incorporates exploratory move operator from Hooke-Jeeves local search method, applied to solve localization in wireless networks, is presented. Moreover, we have compared the proposed improved whale optimization algorithm with its original version, as well as with some other algorithms that were tested on the same model and data sets, in order to evaluate its performance. Simulation results demonstrate that our presented hybridized approach manages to accomplish more accurate and consistent unknown nodes locations in the wireless networks topology, than other algorithms included in comparative analysis.

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References

  1. Ahmed, A., Ali, J., Raza, A., Abbas, G.: Wired vs wireless deployment support for wireless sensor networks. In: TENCON IEEE Region 10 Conference, pp. 1–3 (2006)

    Google Scholar 

  2. Arora, S., Singh, S.: Node localization in wireless sensor networks using butterfly optimization algorithm. Arab. J. Sci. Eng. 42(8), 3325–3335 (2017)

    Article  Google Scholar 

  3. Goyal, S., Patterh, M.S.: Wireless sensor network localization based on cuckoo search algorithm. Wireless Pers. Commun. 79, 223–234 (2014)

    Article  Google Scholar 

  4. Hooke, R., Jeeves, T.A.: “Direct Search” solution of numerical and statistical problems. J. ACM 8(2), 212–229 (1961)

    Article  MATH  Google Scholar 

  5. Lavanya, D., Udgata, S.K.: Swarm intelligence based localization in wireless sensor networks. Springer 79, 317–328 (2011)

    Google Scholar 

  6. Ling, Y., Zhou, Y., Luo, Q.: Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5, 6168–6186 (2017)

    Article  Google Scholar 

  7. Liu, C., Liu, S., Zhang, W., Zhao, D.: The performance evaluation of hybrid localization algorithm in wireless sensor networks. Mob. Netw. Appl. 21(6), 994–1001 (2016)

    Article  Google Scholar 

  8. Mafarja, M.M., Mirjalili, S.: Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260, 302–312 (2017)

    Article  Google Scholar 

  9. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  10. Strumberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M.: Hybridized moth search algorithm for constrained optimization problems. In: International Young Engineers Forum (YEF-ECE), pp. 1–5 (2018)

    Google Scholar 

  11. Strumberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M.: Monarch butterfly optimization algorithm for localization in wireless sensor networks. In: 28th IEEE International Conference Radio Elektronika, pp. 1–6 (2018)

    Google Scholar 

  12. Strumberger, I., Bacanin, N., Tuba, M.: Hybridized elephant herding optimization algorithm for constrained optimization. In: Hybrid Intelligent Systems. AISC, vol. 734, pp. 158–166. Springer, Cham (2018)

    Google Scholar 

  13. Strumberger, I., Beko, M., Tuba, M., Minovic, M., Bacanin, N.: Elephant herding optimization algorithm for wireless sensor network localization problem. In: Technological Innovation for Resilient Systems, pp. 175–184. Springer, Cham (2018)

    Google Scholar 

  14. Strumberger, I., Minovic, M., Tuba, M., Bacanin, N.: Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors 19(11), 2515 (2019)

    Article  Google Scholar 

  15. Strumberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M.: Modified and hybridized monarch butterfly algorithms for multi-objective optimization. In: Hybrid Intelligent Systems. AISC, vol. 923, pp. 449–458. Springer, Cham (2020)

    Google Scholar 

  16. Strumberger, I., Tuba, E., Zivkovic, M., Bacanin, N., Beko, M., Tuba, M.: Dynamic search tree growth algorithm for global optimization. In: Technological Innovation for Industry and Service Systems. IFIP AICT, vol. 553, pp. 143–153. Springer, Cham (2019)

    Google Scholar 

  17. Strumberger, I., Tuba, M., Bacanin, N., Tuba, E.: Cloudlet scheduling by hybridized monarch butterfly optimization algorithm. J. Sensor Actuator Netw. 8(3), 1–44 (2019)

    Google Scholar 

  18. Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N., Tuba, M.: Mobile robot path planning by improved brain storm optimization algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2018)

    Google Scholar 

  19. Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Bare bones fireworks algorithm for capacitated p-median problem. In: Advances in Swarm Intelligence. LNCS, vol. 10941, pp. 283–291. Springer, Cham (2018)

    Google Scholar 

  20. Tuba, E., Tuba, M., Beko, M.: Support vector machine parameters optimization by enhanced fireworks algorithm. In: Advances in Swarm Intelligence. LNCS, vol. 9712, pp. 526–534. Springer, Cham (2016)

    Google Scholar 

  21. Tuba, M., Bacanin, N.: JPEG quantization tables selection by the firefly algorithm. In: International Conference on Multimedia Computing and Systems (ICMCS), pp. 153–158. IEEE (2014)

    Google Scholar 

  22. Tuba, M., Bacanin, N., Beko, M.: Multi-objective RFID network planning by artificial bee colony algorithm with genetic operators. In: Advances in Swarm and Computational Intelligence. LNCS. vol. 9140, pp. 247–254. Springer, Cham (2015)

    Google Scholar 

  23. Zivkovic, M., Branovic, B., Markovic, D., Popovic, R.: Energy efficient security architecture for wireless sensor networks. In: 20th Telecommunications Forum (TELFOR), pp. 1524–1527 (2012)

    Google Scholar 

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Acknowledgment

The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

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Authors and Affiliations

  1. Singidunum University, 11000, Belgrade, Serbia

    Nebojsa Bacanin, Eva Tuba, Miodrag Zivkovic, Ivana Strumberger & Milan Tuba

Authors
  1. Nebojsa Bacanin
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  2. Eva Tuba
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  3. Miodrag Zivkovic
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  4. Ivana Strumberger
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  5. Milan Tuba
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Corresponding author

Correspondence to Milan Tuba .

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Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. School of Computer Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India

    Shishir K. Shandilya

  3. Area of Project Engineering, University of Cordoba, Córdoba, Spain

    Laura Garcia-Hernandez

  4. Escola de Engenharia, Universidade do Minho, Guimarães, Portugal

    Maria Leonilde Varela

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Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., Tuba, M. (2021). Whale Optimization Algorithm with Exploratory Move for Wireless Sensor Networks Localization. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_33

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