Efficient computation of wireless sensor network lifetime through deep neural networks


The most important quality-of-service metric for wireless sensor networks (WSNs), arguably, is the lifetime. Estimating the network lifetime under optimal operation conditions can be done by casting the problem as a mixed integer programming (MIP) model and solving the problem instances to optimality. Yet, solution times of such models are excessively high. Therefore, it is not possible to work with large problem instances within an acceptable solution time. Adopting learning based algorithms has the ability to produce near-optimal results much more rapidly in comparison to MIP models. In this study, we propose a deep neural network (DNN) based model to determine the WSN lifetime near-optimally virtually instantly. The proposed model is able to predict the lifetime of a randomly deployed WSN over a predetermined area with an average accuracy more than 98.5%. An interesting outcome of the study is that the DNN based model is able to estimate the lifetime of WSNs with higher number of nodes successfully even if it is trained with a dataset obtained with lower number of nodes.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Akbas, A., Yildiz, H. U., Ozbayoglu, A. M., & Tavli, B. (2019). Neural network based instant parameter prediction for wireless sensor network optimization models. Wireless Networks, 25(6), 3405–3418.

    Article  Google Scholar 

  2. 2.

    Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3, 325–349.

    Article  Google Scholar 

  3. 3.

    Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys and Tutorials, 16(4), 1996–2018.

    Article  Google Scholar 

  4. 4.

    Alshinina, R. A., & Elleithy, K. M. (2018). A highly accurate deep learning based approach for developing wireless sensor network middleware. IEEE Access, 6, 29885–29898.

    Article  Google Scholar 

  5. 5.

    Chang, J. H., & Tassiulas, L. (2004). Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking, 12(4), 609–619.

    Article  Google Scholar 

  6. 6.

    Correll, J. T. (2004). Igloo white. Air Force Magazine, 87(11), 56–61.

    Google Scholar 

  7. 7.

    Farsi, M., Elhosseini, M. A., Badawy, M., Arafat Ali, H., & Zain Eldin, H. (2019). Deployment techniques in wireless sensor networks, coverage and connectivity: A survey. IEEE Access, 7, 28940–28954.

    Article  Google Scholar 

  8. 8.

    Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., & Hanzo, L. (2017). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys and Tutorials, 19(1), 550–586.

    Article  Google Scholar 

  9. 9.

    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Article  Google Scholar 

  10. 10.

    Optimization G. (2014). Inc., “gurobi optimizer reference manual,” 2015.

  11. 11.

    Otoum, S., Kantarci, B., & Mouftah, H. T. (2019). On the feasibility of deep learning in sensor network intrusion detection. IEEE Networking Letters, 1(2), 68–71.

    Article  Google Scholar 

  12. 12.

    Su, Y., Lu, X., Zhao, Y., Huang, L., & Du, X. (2019). Cooperative communications with relay selection based on deep reinforcement learning in wireless sensor networks. IEEE Sensors Journal, 19(20), 9561–9569.

    Article  Google Scholar 

  13. 13.

    Sun, Z., Zhou, L., & Wang, W. (2018). Learning time-frequency analysis in wireless sensor networks. IEEE Internet of Things Journal, 5(5), 3388–3396.

    Article  Google Scholar 

  14. 14.

    Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K. C., & Hanzo, L. (2019). Thirty years of machine learning: The road to pareto-optimal next-generation wireless networks. Tech. Rep. arXiv:1902.01946 [cs.NI].

  15. 15.

    Wang, Y., Yang, A., Chen, X., Wang, P., Wang, Y., & Yang, H. (2017). A deep learning approach for blind drift calibration of sensor networks. IEEE Sensors Journal, 17(13), 4158–4171.

    Article  Google Scholar 

  16. 16.

    Xiao, L., Sheng, G., Wan, X., Su, W., & Cheng, P. (2019). Learning-based PHY-layer authentication for underwater sensor networks. IEEE Communications Letters, 23(1), 60–63.

    Article  Google Scholar 

  17. 17.

    Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys and Tutorials, 19(2), 828–854.

    Article  Google Scholar 

  18. 18.

    Yildiz, H. U., Temiz, M., & Tavli, B. (2015). Impact of limiting hop count on the lifetime of wireless sensor networks. IEEE Communications Letters, 19(4), 569–572.

    Article  Google Scholar 

  19. 19.

    Yildiz, H. U., Tavli, B., Kahjogh, B. O., & Dogdu, E. (2017). The impact of incapacitation of multiple critical sensor nodes on wireless sensor network lifetime. IEEE Wireless Communications Letters, 6(3), 306–309.

    Article  Google Scholar 

  20. 20.

    Yildiz, H. U., Kurt, S., & Tavli, B. (2019). Comparative analysis of transmission power level and packet size optimization strategies for wsns. IEEE Systems Journal, 13(3), 2264–2274.

    Article  Google Scholar 

  21. 21.

    Zappone, A., Di Renzo, M., & Debbah, M. (2019). Wireless networks design in the era of deep learning: Model-based, ai-based, or both? IEEE Transactions on Communications, 67(10), 7331–7376.

    Article  Google Scholar 

  22. 22.

    Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys and Tutorials, 21(3), 2224–2287.

    Article  Google Scholar 

  23. 23.

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

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Ahmet Murat Ozbayoglu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yilmaz, M., Ozbayoglu, A.M. & Tavli, B. Efficient computation of wireless sensor network lifetime through deep neural networks. Wireless Netw (2021). https://doi.org/10.1007/s11276-021-02556-8

Download citation


  • Wireless sensor networks
  • Network lifetime
  • Lifetime prediction
  • Machine learning
  • Deep neural networks
  • Multi-layer perceptron