Skip to main content

Advertisement

SpringerLink
Log in

An Energy-Aware Model for Wireless Sensor Networks: Hierarchical Compressive Data Gathering for Hierarchical Grid-Based Routing (HCDG-HGR)

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In wireless sensor networks compressive sensing (CS) is an effective alternative technique for traditional data gathering methods which affect the energy consumption by decreasing the whole number and length of data packets transmitted to the sink node. Due to using CS technique in hierarchical routing methods further reduction of energy consumption is achieved. In this work, an energy-aware CS-based data gathering model for hierarchical grid-based routing methods named HCDG-HGR is presented. In this model, a CS-based method is put into the WSN in which routing is performed based on hierarchical routing methods. In this model, network energy consumption is analyzed and practical relationships are formulated to calculate energy consumption of the network at the end of each sampling period. Simulation results confirm that the model effectively reduces the network energy consumption and thus increases the network lifetime with regard to the number of network alive nodes at the end of sampling periods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

All data generated or analysed during this study are included in this published article.

References

  1. Shafiq, M., Ashraf, H., Ullah, A., & Tahira, S. (2020). Systematic literature review on energy efficient routing schemes in WSN: A survey. Mobile Networks and Applications, 25(1), 882–895.

    Article  Google Scholar 

  2. Chan, L., Chavez, K. G., Rudolph, H., & Hourani, A. (2020). Hierarchical routing protocols for wireless sensor network: A compressive survey. Wireless Networks, 26(1), 3291–3314.

    Article  Google Scholar 

  3. Bhushan, B., & Sahoo, G. (2019). Routing protocols in wireless sensor networks. In Computational intelligence in sensor networks (pp. 215–248) first ed., vol. 776, Springer.

  4. Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  5. Candes, E., & Wakin, M. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21–30.

    Article  Google Scholar 

  6. Wakin, M. B., Duarte, M. F., Sarvotham, S., Baron, D., & Baraniuk, R. G. (2009). Recovery of jointly sparse signals from few random. In Proceedings of 15th ACM MobiCom (pp. 145–156).

  7. Candes, E., Romberg, J., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207–1223.

    Article  MathSciNet  MATH  Google Scholar 

  8. Tropp, J., & Gilbert, A. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 4655–4666.

    Article  MathSciNet  MATH  Google Scholar 

  9. Duarte, M., & Baraniuk, R. (2012). Kronecker compressive sensing. IEEE Transactions on Image Processing, 21(2), 494–504.

    Article  MathSciNet  MATH  Google Scholar 

  10. Candes, E. (2008). The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9/10), 589–592.

    Article  MathSciNet  MATH  Google Scholar 

  11. Candes, E., & Romberg, J. (2007). Sparsity and incoherence in compressive sampling. Inverse Problems, 23(3), 969–985.

    Article  MathSciNet  MATH  Google Scholar 

  12. Haupt, J., & Nowak, R. (2006). Signal reconstruction from noisy random projections. IEEE Transactions on Information Theory, 52(9), 4036–4048.

    Article  MathSciNet  MATH  Google Scholar 

  13. Luo, C. et al. (2009) Compressive data gathering for large-scale wireless sensor networks. In Proceediongs of 15th Annual International Conference on Mobile Computing and Networking (Mobicom) (pp. 145–156).

  14. Lan, K.-C., & Wei, M.-Z. (2017). A compressibility-based clustering algorithm for hierarchical compressive data gathering. IEEE Sensors Journal, 17(8), 2550–2562.

    Article  Google Scholar 

  15. Pacharaney, U. S., & Gupta, R. K. (2019). Clustering and compressive data gathering in wireless sensor network. Wireless Personal Communications, 109(2), 1311–1331.

    Article  Google Scholar 

  16. Wang, X., & Chen, H. (2022). A survey of compressive data gathering in WSNs for IoTs. Wireless Communications and Mobile Computing., 25, 2022.

    Google Scholar 

  17. Aziz, A., Singh, K., Osamy, W., & Khedr, A. M. (2020). An efficient compressive sensing routing scheme for internet of things based wireless sensor networks. Wireless Personal Communications, 114(3), 1905–1925.

    Article  Google Scholar 

  18. Ghaderi, M. R., Vakili, V. T., & Sheikhan, M. (2020). FGAF-CDG: Fuzzy geographic routing protocol based on compressive data gathering in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 11, 2567–2589.

    Article  Google Scholar 

  19. Arora, K., Harma, V., & Sachdeva, M. (2016). A survey on LEACH and other’s routing protocols in wireless sensor network. Optik, 127, 6590–6600.

    Article  Google Scholar 

  20. Misra, S., & Kumar, R. (2017) An analytical study of LEACH and PEGASIS protocol in wireless sensor network. In Proceedings of International Conference Innovations in Information, Embedded and Communication Systems (ICIIECS).

  21. Song, Y., Liu, Z. G., He, X. L., & Jiang, H. (2019). Research on data fusion scheme for wireless sensor networks with combined improved LEACH and compressed sensing. Sensors, 19(21), 4704.

    Article  Google Scholar 

  22. Ma, J., Wang, S., Meng, C., Ge, Y., & Du, J. (2018). Hybrid energy-efficient APTEEN protocol based on ant colony algorithm in wireless sensor network. EURASIP Journal on Wireless Communications and Networking, 102, 1–13.

    Google Scholar 

  23. Pawan, S. M., Doja, M. N., & Alam, B. (2020). Fuzzy based enhanced cluster head selection (FBECS) for WSN. Journal of King Saud University: Science, 32(1), 390–401.

    Article  Google Scholar 

  24. Murugaanandam, S., & Ganapathy, V. (2019). Reliability-based cluster head selection methodology using fuzzy logic for performance improvement in WSNs. IEEE Access, 7, 87357–87368.

    Article  Google Scholar 

  25. Hamzah, A., Shurman, M., et al. (2019). Energy-efficient fuzzy-logic-based clustering technique for hierarchical routing protocols in wireless sensor networks. Sensors, 19(561), 1–23.

    Google Scholar 

  26. Kim, J.-M., Park, S.-H., Han, Y.-J., & Chung, T.-M. (2008) CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In Proceedings of IEEE 10th International Conference on Advanced Communication Technology.

  27. Sharma, T., & Kumar, B. (2012). F-MCHEL: Fuzzy based master cluster head election leach protocol in wireless sensor network. International Journal of Computer Science and Telecommunication, 3(10), 8–13.

    Google Scholar 

  28. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.

    Article  Google Scholar 

  29. Purkait, R., & Tripathi, S. (2017). Energy aware fuzzy based multi-hop routing protocol using unequal clustering. Wireless Personal Communications, 94(3), 809–833.

    Article  Google Scholar 

  30. Xie, R., & Jia, X. (2014). Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Transactions on Parallel and Distributed Sys., 25(3), 806–815.

    Article  Google Scholar 

  31. Ghaderi, M. R., Vakili, V. T., & Sheikhan, M. (2021). Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks. Telecommunication Systems, 77, 83–108.

    Article  Google Scholar 

  32. Li, X. L., Tao, X. F., & Chen, Z. (2018). Spatio-temporal compressive sensing-based data gathering in wireless sensor networks. IEEE Wireless Communications Letters, 7(2), 198–201.

    Article  Google Scholar 

  33. Zhang, C., Li, O., Tong, X., Ke, K., & Li, M. X. (2019). Spatiotemporal data gathering based on compressive sensing in WSNs. IEEE Wireless Communications Letters, 8(4), 1252–1255.

    Article  Google Scholar 

  34. Al Fallah, S., Arioua, M., El Oualkadi, A., & El Aasri, J. (2020) On the performance of spatio-temporal compression schemes in cluster-based WSNs. In 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT) (pp. 1–10). IEEE.

  35. Sekar, K., Suganya Devi, K., & Srinivasan, P. (2021). Energy efficient data gathering using spatio-temporal compressive sensing for WSNs. Wireless Personal Communications, 117(2), 1279–1295.

    Article  Google Scholar 

  36. Wei, X., Yan, S., Wang, X., Guizani, M., & Xiaojiang, Du. (2021). STAC: A spatio-temporal approximate method in data collection applications. Pervasive and Mobile Computing, 73, 101371.

    Article  Google Scholar 

Download references

Funding

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran

    Mohammad Reza Ghaderi & Mansour Sheikhan

Authors
  1. Mohammad Reza Ghaderi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mansour Sheikhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Ghaderi.

Ethics declarations

Conflict of interest

The author's declared that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghaderi, M.R., Sheikhan, M. An Energy-Aware Model for Wireless Sensor Networks: Hierarchical Compressive Data Gathering for Hierarchical Grid-Based Routing (HCDG-HGR). Wireless Pers Commun 129, 1645–1668 (2023). https://doi.org/10.1007/s11277-023-10200-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10200-1

Keywords

Search

Navigation