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Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Year 2023, Volume: 11 Issue: 3, 262 - 266, 21.08.2023
https://doi.org/10.17694/bajece.1281060

Abstract

Wireless sensor networks (WSNs) are widely used in various fields, and their deployment is critical to ensure area coverage and full network connectivity to achieve the maximum network lifetime. In this study, we present a mixed-integer programming (MIP) model that deeply investigates deployment parameters to optimize lifetime and analyze network connectivity. We further analyze the obtained results using Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms to achieve higher accuracy rates. Our evaluation shows that the DBN outperforms the DNN with an accuracy rate of 81.2%, precision of 81.2%, recall of 99.1%, and an F1-Score of 0.78. We also utilize two different datasets to justify the efficiency of the DBN in this research. The findings of this study emphasize the validity of our DBN algorithm and encourage further research into lifetime optimization and connectivity analysis in WSNs.

References

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Year 2023, Volume: 11 Issue: 3, 262 - 266, 21.08.2023
https://doi.org/10.17694/bajece.1281060

Abstract

References

  • 1] M. Sheikh-Hosseini and S. R. S. Hashemi, “Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms,” Expert Systems with Applications, p. 116164, 2021.
  • [2] M. R. Senouci and A. Mellouk, “A robust uncertainty-aware clusterbased deployment approach for wsns: Coverage, connectivity, and lifespan,” Journal of Network and Computer Applications, vol. 146, p. 102414, 2019.
  • [3] N. Aitsaadi, N. Achir, K. Boussetta, and G. Pujolle, “Artificial potential field approach in wsn deployment: Cost, qom, connectivity, and lifetime constraints,” Computer Networks, vol. 55, no. 1, pp. 84–105, 2011.
  • [4] S. Sengupta, S. Das, M. Nasir, and B. K. Panigrahi, “Multi-objective node deployment in wsns: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 405–416, 2013.
  • [5] C. Sevgi and A. Koc¸yi˘git, “Optimal deployment in randomly deployed heterogeneous wsns: A connected coverage approach,” Journal of Network and Computer Applications, vol. 46, pp. 182–197, 2014.
  • [6] A. Akbas, H. U. Yildiz, and B. Tavli, “Data packet length optimization for wireless sensor network lifetime maximization,” in 2014 10th International Conference on Communications (COMM). IEEE, 2014, pp. 1–6.
  • [7] O. G. Uyan, A. Akbas, and V. C. Gungor, “Machine learning approaches for underwater sensor network parameter prediction,” Ad Hoc Networks, vol. 144, p. 103139, 2023.
  • [8] A. Akbas, H. U. Yildiz, A. M. Ozbayoglu, and B. Tavli, “Neural network based instant parameter prediction for wireless sensor network optimization models,” Wireless Networks, vol. 25, no. 6, pp. 3405–3418, 2019.
  • [9] “Gams,” https://www.gams.com/products/gams/gams-language/, 12 2021, (Accessed on 12/12/2021).
  • [10] “Log distance path loss or log normal shadowing model - gaussianwaves,” https://www.gaussianwaves.com/2013/09/ log-distance-path-loss-or-log-normal-shadowing-model/, 12 2021, (Accessed on 12/12/2021).
  • [11] A. Akbas, H. U. Yildiz, B. Tavli, and S. Uludag, “Joint optimization of transmission power level and packet size for wsn lifetime maximization,” IEEE Sensors Journal, vol. 16, no. 12, pp. 5084–5094, 2016.
  • [12] “Matlab - mathworks - matlab & simulink,” https://www.mathworks. com/products/matlab.html, 12 2021, (Accessed on 12/12/2021).
  • [13] M. Gentil, A. Galeazzi, F. Chiariotti, M. Polese, A. Zanella, and M. Zorzi, “A deep neural network approach for customized prediction of mobile devices discharging time,” in GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, 2017, pp. 1–6.
  • [14] C.-H. Zhu and J. Zhang, “Developing soft sensors for polymer melt index in an industrial polymerization process using deep belief networks,” International Journal of Automation and Computing, vol. 17, no. 1, pp. 44–54, 2020.
  • [15] H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, “Unsupervised learning of hierarchical representations with convolutional deep belief networks,” Communications of the ACM, vol. 54, no. 10, pp. 95–103, 2011.
  • [16] Y. Qin, X. Wang, and J. Zou, “The optimized deep belief networks with improved logistic sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines,” IEEE Transactions on Industrial Electronics, vol. 66, no. 5, pp. 3814–3824, 2018.
  • [17] M. Hossin and M. N. Sulaiman, “A review on evaluation metrics for data classification evaluations,” International journal of data mining & knowledge management process, vol. 5, no. 2, p. 1, 2015.
  • [18] D. Justus, J. Brennan, S. Bonner, and A. S. McGough, “Predicting the computational cost of deep learning models,” in 2018 IEEE international conference on big data (Big Data). IEEE, 2018, pp. 3873–3882.
There are 18 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Araştırma Articlessi
Authors

Ayhan Akbaş 0000-0002-6425-104X

Selim Buyrukoğlu 0000-0001-7844-3168

Early Pub Date August 20, 2023
Publication Date August 21, 2023
Published in Issue Year 2023 Volume: 11 Issue: 3

Cite

APA Akbaş, A., & Buyrukoğlu, S. (2023). Deep Belief Network Based Wireless Sensor Network Connectivity Analysis. Balkan Journal of Electrical and Computer Engineering, 11(3), 262-266. https://doi.org/10.17694/bajece.1281060

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