Skip to main content

Advertisement

SpringerLink
Log in

MSoC: Multi-scale Optimized Clustering for Energy Preservation in Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Energy efficient clustering has always been the center of attention among the research community pertaining to wireless sensor network (WSN). Till last decade, there have been significant studies towards clustering technique as well as energy efficiency, but no robust solution has yet been evolved. Therefore, this manuscript introduces a unique optimization scheme for the purpose of enhancing the clustering techniques. The technique is called as MSoC or multi-scale optimized clustering, where a novel clustering technique is shown with an aid of single and multi-level clustering approximation method. The technique also introduces a concept of RF Transceiver that can solve the energy problems in data aggregation for large scale WSN. The result acquired from the study exhibits to better performance with respect to energy conservation on higher number of simulation rounds till date in comparison to existing techniques.

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

Similar content being viewed by others

References

  1. Schieferdecker, D. (2014). An algorithmic view on sensor networks: Surveillance, localization, and communication. Dissertation, Institut für Theoretische Informatik (ITI).

  2. Rocker, C. (2010). Smart healthcare applications and services: Developments and practices. Pennsylvania: IGI Global.

    Google Scholar 

  3. Agrawal, D. P., & Zeng, Q.-A. (2015). Introduction to wireless and mobile systems. Boston: Cengage Learning.

    Google Scholar 

  4. El Emary, I. M. M., & Ramakrishnan, S. (2013). Wireless sensor networks: From theory to applications. Boca Raton: CRC Press.

    Book  Google Scholar 

  5. Sholla, S. (2015). Performance evaluation of clustering algorithms in wireless sensor networks (WSN). Energy efficiency of S-Web and LEACH. Munich: GRIN Verlag.

    Google Scholar 

  6. Varshney, S., Kumar, C., & Swaroop, A. (2015). A comparative study of hierarchical routing protocols in wireless sensor networks. In 2015 2nd international conference on computing for sustainable global development (INDIACom), New Delhi (pp. 1018–1023).

  7. Liu, X. (2015). Atypical hierarchical routing protocols for wireless sensor networks: A review. IEEE Sensors Journal, 15(10), 5372–5383.

    Article  Google Scholar 

  8. Singh, S. P., & Sharma, S. C. (2015). A survey on cluster based routing protocols in wireless sensor networks. In International conference on advanced computing technologies and applications (Vol. 45, pp. 687–695). Elsevier.

  9. Cecilio, J., Costa, J., & Furtado, P. (2010). Survey on data routing in wireless sensor networks. In T. Hara, V. I. Zadorozhny, & E. Buchmann (Eds.), Wireless sensor network technologies for the information explosion era (Vol. 278, pp. 3–46). Berlin: Springer.

    Chapter  Google Scholar 

  10. Reddy, M. J., Prakash, P. S., & Reddy, P. C. (2012). Homogeneous and heterogeneous energy schemes for hierarchical cluster based routing protocols in WSN: A survey. In Proceedings of the third international conference on trends in information, telecommunication and computing (Vol. 150, pp. 591–595). Springer.

  11. Jyothi, A. P., & Usha, S. (2015). Trends and technologies used for mitigating energy efficiency issues in wireless sensor network. International Journal of Computer Applications, 111(3), 32–40.

    Article  Google Scholar 

  12. Meenakshi, D., & Kumar, S. (2012). Energy efficient hierarchical clustering routing protocol for wireless sensor networks. In International conference on computer science and information technology. Social informatics and telecommunications engineering (pp. 409–420). Springer.

  13. Patil, P. R., & Kulkarni, U. P. (2014). Energy-efficient cluster-based aggregation protocol for heterogeneous wireless sensor networks. In Intelligent computing, networking, and informatics. Advances in intelligent systems and computing. Springer.

  14. Neamatollahi, P., Taheri, H., & Naghibzadeh, M. (2011). DESC: Distributed energy efficient scheme to cluster wireless sensor networks. In International conference on wired/wireless internet communications (pp. 234–246). Springer.

  15. Saleem, M., Caro, G. A. D., & Farooq, M. (2011). Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Information Sciences, 181, 4597–4624.

    Article  Google Scholar 

  16. Mohajerani, A., & Gharavian, D. (2015). An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks. Journal of Wireless Networks, 8, 2637–2647.

    Google Scholar 

  17. Kulkarni, R. V., & Venayagamoorthy, G. K. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics Part C (Applications and Reviews), 41(2), 262–267.

    Article  Google Scholar 

  18. Bharathi, M. A., Vijayakumar, B. P., & Manjaiah, D. H. (2013). Cluster based data aggregation in WSN using swarm optimization technique. International Journal of Engineering and Innovative Technology (IJEIT), 2(12), 140–144.

    Google Scholar 

  19. Bharathia, M. A., Mallikarjunab, M., & Vijaya Kumar, B. P. (2012). Bio-inspired approach for energy utilization in wireless sensor networks. In International conference on modelling optimization and computing (Vol. 38, pp. 3864–3868).

  20. Pitchaimanickam, B., & Radhakrishnan, S. (2013). Bacteria foraging algorithm based clustering in wireless sensor networks. In 2013 fifth international conference on advanced computing (ICoAC), Chennai (pp. 190–195).

  21. Seelam, K., Sailaja, M., & Madhu, T. (2015). An improved BAT-optimized cluster-based routing for wireless sensor networks. In D. Mandal, R. Kar, S. Das, & B. Panigrahi (Eds.), Intelligent computing and applications. Advances in intelligent systems and computing. Berlin: Springer.

    Google Scholar 

  22. Zhu, X., Shen, L., & Peter Yum, T.-S. (2009). Hausdorff clustering and minimum energy routing for wireless sensor networks. IEEE Transactions on Vehicular Technology, 58(2), 990–997.

    Article  Google Scholar 

  23. Adnan, Md. A., Razzaque, M. A., Abedin, Md. A., Reza, S. M. S., & Hussein, M. R. (2016). A novel cuckoo search based clustering algorithm for wireless sensor networks. In Advanced computer and communication engineering technology. Lecture notes in electrical engineering. Springer.

  24. Wei, D., Jin, Y., Vural, S., & Moessner, K. (2011). An energy-efficient clustering solution for wireless sensor networks. IEEE Transactions on Wireless Communications, 10(11), 3973–3983.

    Article  Google Scholar 

  25. Pei, E., Han, H., Sun, Z., Shen, B., & Zhang, T. (2015). LEAUCH: Low-energy adaptive uneven clustering hierarchy for cognitive radio sensor network. EURASIP Journal on Wireless Communications and Networking, 1, 1–8.

    Google Scholar 

  26. Yu, J., Qi, Y., & Wang, G. (2011). An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks. Journal of Control Theory Application, 9(1), 133–139.

    Article  MathSciNet  Google Scholar 

  27. Udompongsuk, K., So-In, C., & Phaudphut, C. (2014). MAP: An optimized energy-efficient cluster header selection technique for wireless sensor networks. In Advances in computer science and its applications. Lecture notes in electrical engineering. Springer.

  28. Jyothi, A. P., & Usha, S. (2017). CFCLP—A novel clustering framework based on combinatorial approach and linear programming in wireless sensor network. In 2017 2nd IEEE international conference on computing and communications technologies (ICCCT), Chennai (pp. 49–54).

  29. Gautam, N., Sofat, S., & Vig, R. (2014). An ant Voronoi based clustering approach for wireless sensor networks. In International conference on ad hoc networks. Social informatics and telecommunications. Springer.

  30. Fu, L., & Medico, E. (2007). FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics, 8(1), 3.

    Article  Google Scholar 

  31. Jyothi, A. P., & Usha, S. (2015). Energy optimization in sensor network using fuzzy local approximation membership algorithm. International Journal of Applied Engineering Research, 10(86), 40–45.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, RRCE Research Centre, Bengaluru, Karnataka, India

    A. P. Jyothi

  2. VTU, Belagavi, Karnataka, India

    A. P. Jyothi

  3. Department of Computer Science and Engineering, RRCE, Bengaluru, Karnataka, India

    S. Usha

Authors
  1. A. P. Jyothi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Usha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. P. Jyothi.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jyothi, A.P., Usha, S. MSoC: Multi-scale Optimized Clustering for Energy Preservation in Wireless Sensor Network. Wireless Pers Commun 105, 1309–1328 (2019). https://doi.org/10.1007/s11277-019-06146-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06146-y

Keywords

Search

Navigation