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Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation

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Abstract

In wireless sensor network projects, it is generally desired to cover the area to be monitored at a given cost and to achieve the maximum useful network lifetime. In the deployment of the wireless sensors, it is necessary to know in advance how many sensor nodes will be required, how much the distance between the nodes should be, etc., or what the transmit power level should be, etc. depending on the channel parameters of the area. This necessitates accurate calculation of variables such as maximum network lifetime, communication channel parameters, number of nodes to be used, and distance between nodes. As numbers reach to the order of hundreds, calculation tends to a NP hard problem to solve. At this point, we employed both single-based and stacked ensemble-based machine learning models to speed up the parameter estimations with highly accurate outcomes. Adaboost was superior over other models (Elastic Net, SVR) in single-based models. Stacked ensemble models achieved best results for the WSN parameter prediction compared to single-based models.

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References

  1. Alsheikh, M.A.; Lin, S.; Niyato, D.; Tan, H.-P.: Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutor. 16(4), 1996–2018 (2014)

    Article  Google Scholar 

  2. Sujanthi, S.; Kalyani, S.N.: SecDL: QoS-aware secure deep learning approach for dynamic cluster-based routing in WSN assisted IoT. Wirel. Pers. Commun. 114(3), 2135–2169 (2020)

    Article  Google Scholar 

  3. Ghate, V.V.; Vijayakumar, V.: Machine learning for data aggregation in WSN: a survey. Int. J. Pure Appl. Math. 118(24), 1–12 (2018)

    Google Scholar 

  4. Varman, S.A.M.; Baskaran, A.R.; Aravindh, S.; Prabhu, E.: Deep learning and IoT for smart agriculture using WSN. In: 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–6. IEEE (2017)

  5. Zhao, L.; Huang, H.; Li, X.; Ding, S.; Zhao, H.; Han, Z.: An accurate and robust approach of device-free localization with convolutional autoencoder. IEEE Internet Things J. 6(3), 5825–5840 (2019)

    Article  Google Scholar 

  6. Zhu, B.; Bedeer, E.; Nguyen, H.H.; Barton, R.; Henry, J.: UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning. IEEE Trans. Veh. Technol. (2021)

  7. Sengupta, S.; Das, S.; Nasir, M.D.; Panigrahi, B.K.: Multi-objective node deployment in WSNs: in search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Eng. Appl. Artif. Intell. 26(1), 405–416 (2013). https://doi.org/10.1016/j.engappai.2012.05.018

    Article  Google Scholar 

  8. Sheikh-Hosseini, M.; Samareh-Hashemi, S.R.: Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms. Expert Syst. Appl. 190, 116164 (2022). https://doi.org/10.1016/j.eswa.2021.116164

    Article  Google Scholar 

  9. Senouci, M.R.; Mellouk, A.: A robust uncertainty-aware cluster-based deployment approach for WSNs: coverage, connectivity, and lifespan. J. Netw. Comput. Appl. 146(June), 102414 (2019). https://doi.org/10.1016/j.jnca.2019.102414

    Article  Google Scholar 

  10. Aitsaadi, N.; Achir, N.; Boussetta, K.; Pujolle, G.: Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints. Comput. Netw. 55(1), 84–105 (2011). https://doi.org/10.1016/j.comnet.2010.07.017

    Article  Google Scholar 

  11. Akbas, A.; Yildiz, H.U.; Ozbayoglu, A.M.; Tavli, B.: Neural network based instant parameter prediction for wireless sensor network optimization models. Wirel. Netw. 25(6), 3405–3418 (2019)

    Article  Google Scholar 

  12. Yilmaz, M.; Ozbayoglu, A.M.; Tavli, B.: Efficient computation of wireless sensor network lifetime through deep neural networks. Wirel. Netw. 27(3), 2055–2065 (2021)

    Article  Google Scholar 

  13. Ben Arbi, I.; Derbel, F.; Strakosch, F.: Forecasting methods to reduce energy consumption in WSN. In: 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6 (2017). https://doi.org/10.1109/I2MTC.2017.7969960

  14. Akbas, A.; Yildiz, H.U.; Tavli, B.; Uludag, S.: Joint optimization of transmission power level and packet size for WSN lifetime maximization. IEEE Sens. J. 16(12), 5084–5094 (2016)

    Article  Google Scholar 

  15. James, G.; Witten, D.; Hastie, T.; Tibshirani, R.: An Introduction to Statistical Learning, vol. 112. Springer (2013)

  16. Zhang, Z.; Lai, Z.; Xu, Y.; Shao, L.; Wu, J.; Xie, G.-S.: Discriminative elastic-net regularized linear regression. IEEE Trans. Image Process. 26(3), 1466–1481 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  17. Ma, J.; Theiler, J.; Perkins, S.: Accurate on-line support vector regression. Neural Comput. 15(11), 2683–2703 (2003)

    Article  MATH  Google Scholar 

  18. Awad, M.; Khanna, R.: Support vector regression. In: Efficient Learning Machines, pp. 67–80. Springer (2015)

  19. Drucker, H.: Improving regressors using boosting techniques. In: ICML, vol. 97, pp. 107–115. Citeseer (1997)

  20. Solomatine, D.P.; Shrestha, D.L.: Adaboost. rt: a boosting algorithm for regression problems. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), vol. 2, pp. 1163–1168. IEEE (2004)

  21. Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media (2019)

  22. Lauret, P.; Heymes, F.; Forestier, S.; Aprin, L.; Pey, A.; Perrin, M.: Forecasting powder dispersion in a complex environment using artificial neural networks. Process Saf. Environ. Prot. 110, 71–76 (2017)

    Article  Google Scholar 

  23. Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T.-Y.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3146–3154 (2017)

    Google Scholar 

  24. Botchkarev, A.: Evaluating performance of regression machine learning models using multiple error metrics in azure machine learning studio. Available at SSRN 3177507 (2018)

  25. Hyndman, R.J.; Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)

    Article  Google Scholar 

  26. Zian, S.; Kareem, S.A.; Varathan, K.D.: An empirical evaluation of stacked ensembles with different meta-learners in imbalanced classification. IEEE Access (2021)

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Author notes

  1. Selim Buyrukoglu contributed equally to this work.

Authors and Affiliations

  1. Computer Engineering Department, Abdullah Gul University, Sumer Campus, Kayseri, 38080, Turkey

    Ayhan Akbas

  2. Computer Engineering Department, Cankiri Karatekin University, Uluyazi Campus, Cankiri, 18100, Turkey

    Selim Buyrukoglu

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  1. Ayhan Akbas
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  2. Selim Buyrukoglu
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Correspondence to Ayhan Akbas.

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Akbas, A., Buyrukoglu, S. Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation. Arab J Sci Eng 48, 9739–9748 (2023). https://doi.org/10.1007/s13369-022-07365-5

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