Skip to main content
Log in

Integrated CS-clustering mechanism for network lifetime improvisation in WSN

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Wireless Sensor network has become hub for the industry and academia people due to its vibrant application and various characteristics like low cost, distributable, low-power technology, data compression and especially wireless communication. Moreover, in terms of application, it provides huge diversified monitoring flexibility for several important field like battlefield, agricultural monitoring, medical monitoring and environmental monitoring. Despite of such large application, there has been constant concern regarding the network lifetime and energy consumption is directly responsible for such issue. Meanwhile compressive sensing has been one of the popular data aggregation mechanism to reduce the data redundancy; hence, this research work design and develop a mechanism named ICCM (Integrated CS-clustering mechanism) which incorporates the clustering and compressive sensing mechanism to design and efficient WSN architecture which aims at network lifetime enhancement through Compressive sensing along with clustering. In ICCM approach, Cluster Heads utilize the novel and optimal CS mechanism for data transmission to Base station; further an novel optimized clustering approach is used for efficient clustering, also we design standalone logical link for data transmission. Furthermore, ICCM is evaluated considering the different parameter like network lifetime, energy consumption, functioning node and non-functioning node; also, comparative analysis with the existing model suggest that ICCM simply outperforms the existing model.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3 
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Al-Karaki JN, Al-Mashaqbeh GA (2007) SENSORIA: a new simulation platform for wireless sensor networks. 2017 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), Valencia, Spain, pp 424–429. https://doi.org/10.1109/SENSORCOMM.2007.4394958

  2. Bouyer A, Masdari M et al (2015) A new approach for decreasing energy in wireless sensor networks with hybrid LEACH protocol and fuzzy C-means algorithm. Int J Commun Netw Distrib Syst 14(4):400–412. https://doi.org/10.1504/IJCNDS.2015.069675

    Article  Google Scholar 

  3. Dehghani S, Barekatain B et al (2018) An enhanced energyaware cluster-based routing algorithm in wireless sensor networks. Wirel Pers Commun 98(1):1605–1635. https://doi.org/10.1007/s11277-017-4937-1

    Article  Google Scholar 

  4. Gupta HP, Rao SV et al (2015) Geographic routing in clustered wireless sensor networks among obstacles. IEEE Sens J 15(5):2984–2992. https://doi.org/10.1109/JSEN.2014.2385734

    Article  Google Scholar 

  5. Jain N, Gupta A et al (2019) iDEG: integrated data and energy gathering framework for practical wireless sensor networks using compressive sensing. IEEE Sens J 19(3):1040–1051. https://doi.org/10.1109/JSEN.2018.2878788

  6. Jiawei T, Anfeng L et al (2018) A trust-based secure routing scheme using the traceback approach for energy-harvesting wireless sensor networks. Sensors 18(3):1–43. https://doi.org/10.3390/s18030751

    Article  Google Scholar 

  7. Lin D, Min W et al (2020) An energy-efficient routing method in WSNs based on compressive sensing: from the perspective of social welfare. IEEE Embed Syst Lett. https://doi.org/10.1109/LES.2020.3022848

  8. Liu X, Qiu T et al (2020) Latencyaware path planning for disconnected sensor networks with mobile sinks. IEEE Trans Ind Informat 16(1):350–361. https://doi.org/10.1109/TII.2019.2916300

    Article  Google Scholar 

  9. Mukherjee A, Goswami P, Yang L et al (2020) Deep neural network-based clustering technique for secure IIoT. Neural Comput&Applic 32:16109–16117. https://doi.org/10.1007/s00521-020-04763-4

    Article  Google Scholar 

  10. Qiao J, Zhang X (2018) Compressive data gathering based on even clustering for wireless sensor networks. IEEE Access 6:24391–24410. https://doi.org/10.1109/ACCESS.2018.2832626

  11. Reddy V, Gayathri P (2019) Integration of Internet of Things with wireless sensor network. Int J Electr Comput Eng 9(1):439–444. https://doi.org/10.11591/ijece.v9i1.pp439-444

  12. Shen J, Wang A et al (2017) An efficient centroid-based routing protocol for energy management in WSN-assisted IoT. IEEE Access 5:18469–18479. https://doi.org/10.1109/ACCESS.2017.2749606

    Article  Google Scholar 

  13. Sheta A, Solaiman B (2015) Evolving a hybrid K-means clustering algorithm for wireless sensor network using PSO and GAs. Int J Comput Sci Issues 12(1):23–32. https://doi.org/10.1109/SAI.2015.7237270

    Article  Google Scholar 

  14. Su S, Zhao S (2018) An optimal clustering mechanism based on fuzzy-C means for wireless sensor networks. Sustain Comput Inf Syst 18:127–134. https://doi.org/10.1016/J.SUSCOM.2017.08.001

    Article  Google Scholar 

  15. Tangand L, Baijun W et al (2017) Low-cost collaborative mobile charging for large-scale WSNss. IEEE Trans Mobile Comput 16:2213–2227. https://doi.org/10.17148/IJARCCE.2019.8303

    Article  Google Scholar 

  16. Tian W, Dan Z et al (2019) Bidirectional prediction based underwater data collection protocol for end-edgecloud orchestrated system. IEEE Trans Ind Informat to be published. https://doi.org/10.1109/TII.2019.2940745

  17. Tian W, Haoxiong K et al (2020) Big data cleaning based on mobile edge computing in industrial sensor-cloud. IEEE Trans Ind Informat 16(2):1321–1329. https://doi.org/10.1109/TII.2019.2938861

    Article  Google Scholar 

  18. Tinker MS, Chinara S (2015) Energy conservation clustering in wireless sensor networks for increased life time. In: Proc. 2nd Int. Conf. Adv. Comput. Commun. Eng., pp 7–10. https://doi.org/10.1109/ACCESS.2020.3035624

  19. Wang Q, Guo S et al (2018) Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks. EURASIP J Wirel Commun Netw 2018(1):1–11. https://doi.org/10.1186/s13638-018-1067-8

    Article  Google Scholar 

  20. Wu Y, Huang H et al (2020) An incentive-based protection and recovery strategy for secure big data in social networks. Inf Sci 508:79–91. https://doi.org/10.1016/j.ins.2019.08.064

    Article  Google Scholar 

  21. Xuxun L, Anfeng L et al (2020) Restoring connectivity of damaged sensor networks for long-term survival in hostile environments. IEEE Internet Things J to be published. https://doi.org/10.1109/JIOT.2019.2953476

  22. Yalin N, Haijun W et al (2014) Data-smoothness based preprocessing strategy for wavelet data processing in WSNss. J Commun 9(10):762–770. https://doi.org/10.12720/jcm

    Article  Google Scholar 

  23. Yalin N, Sanyang L et al (2014) Data preprocessing algorithm for better Haar-based data compression in wireless sensor networks. Sens Lett 12(2):287–293. https://doi.org/10.1166/sl.2014.3281

    Article  Google Scholar 

  24. Yalin N, Haijun W et al (2017) Distributed and morphological operation-based data collection algorithm. Int J Distrib Sensor Netw 13(7):1–16. https://doi.org/10.1177/1550147717717593

    Article  Google Scholar 

  25. Zeyu S, Guozeng Z et al (2019) PM-LPDR: a prediction model for lost packets based on data reconstruction on lossy links in sensor networks. Int J Comput Sci Eng 19(2):177–188. https://doi.org/10.1504/IJCSE.2019.100238

  26. Zeyu S, Xiaofei X (2018) ENCP: a new energy-efficient nonlinear coverage control protocol in mobile sensor networks. EURASIP J Wirel Commun Netw 2018:1–15. https://doi.org/10.1186/s13638-018-1023-7

  27. Zeyu S, Rong T et al (2018) CS-PLM: compressive sensing data gathering algorithm based on packet loss matching in sensor networks. Wirel Commun Mobile Comput 2018. https://doi.org/10.1155/2018/5131949

  28. Zeyu S, Xiaofei X et al (2019) An optimized clustering communication protocol based on intelligent computing information-centric Internet of Things. IEEE Access 7:28238–28249. https://doi.org/10.1109/ACCESS.2019.2896250

    Article  Google Scholar 

  29. Zhang P, Wang J (2019) On enhancing network dynamic adaptability for compressive sensing in WSNs. IEEE Trans Commun 67(12):8450–8459. https://doi.org/10.1109/TCOMM.2019.2938950

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nandini S. Patil.

Ethics declarations

Conflicts of interest/Competing interests

 All authors declare that we have no conflicts 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

Patil, N.S., Parveen, A. Integrated CS-clustering mechanism for network lifetime improvisation in WSN. Multimed Tools Appl 82, 19487–19502 (2023). https://doi.org/10.1007/s11042-022-14261-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14261-5

Keywords

Navigation