Skip to main content

Advertisement

SpringerLink
Log in

MeMLO: Mobility-Enabled Multi-level Optimization Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The paper presents a technique called as Mobility-Enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimization attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational complexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm.

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

Similar content being viewed by others

References

  1. Selvi, G. V., & Manoharan, R. (2013). A survey of energy efficient unequal clustering algorithms for wireless sensor networks. International Journal of Computer Applications, 2, 1–4.

    Google Scholar 

  2. Kumar, V., Bansal, M., & Rai, M. K. (2015). Literature survey on energy efficient clustering and routing in wireless sensor networks. International Journal of Computer Application, 5(3), 1–5.

    Article  Google Scholar 

  3. Anwar, A., & Sridharan, D. (2015). A survey on routing protocols for wireless sensor networks in various environments. International Journal of Computer Applications, 112(5), 13–29.

    Google Scholar 

  4. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocols for wireless microsensor networks. In Proceedings of the 33rd Hawaaian international conference on systems science (HICSS).

  5. Lindsey, S., & Raghavendra, C. (2002). PEGASIS: Power- efficient gathering in sensor information systems. In IEEE aerospace conference proceedings, pp. 1125–1130.

  6. Manjeshwar, A., & Agarwal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In 1st international workshop on parallel and distributed computing issues in wireless networks and mobile computing.

  7. Manjeshwar, A., & Agarwal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Parallel and distributed processing symposium., proceedings international, IPDPS 2002, pp. 195–202.

  8. Younis, O., & Fahmy, S. (2004). Distributed clustering in ad hoc sensor networks: A hybrid, energy-efficient approach. In Proceedings of IEEE INFOCOM, Hong Kong, an extended version appeared in IEEE Transactions on Mobile Computing (Vol. 3, Issue 4).

  9. Rajalakshmi, M. C. (2014). Review of typical power conservation techniques in wireless sensor network. International Journal of Computer Applications, 88(10), 7–13.

    Article  Google Scholar 

  10. Adnan, M. A., Razzaque, M. A., Abedin, M. A., Reza, S. M. S., & Hussein, M. R. (2016). A novel Cuckoo search based clustering algorithm for wireless sensor networks. In H. Sulaiman, M. Othman, M. Othman, Y. Rahim & N. Pee (Eds.), Advanced computer and communication engineering technology. Lecture notes in electrical engineering (Vol. 362). Cham: Springer.

  11. 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 (Vol. 343). New Delhi: Springer.

  12. 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, 2015(1), 122.

    Article  Google Scholar 

  13. Udompongsuk, K., So-In, C., Phaudphut, C., Rujirakul, K., Soomlek, C., & Waikham, B. (2014). MAP: An optimized energy-efficient cluster header selection technique for wireless sensor networks. In H. Jeong, M. S. Obaidat, N. Yen & J. Park (Eds.), Advances in computer science and its applications. Lecture notes in electrical engineering (Vol. 279). Berlin: Springer.

  14. Patil, P. R., & Kulkarni, U. P. (2014). Energy-efficient cluster-based aggregation protocol for heterogeneous wireless sensor networks. In D. Mohapatra & S. Patnaik (Eds.), Intelligent computing, networking, and informatics. Advances in Intelligent Systems and Computing (Vol. 243). New Delhi: Springer.

  15. Gautam, N., Sofat, S., & Vig, R. (2014). An ant voronoi based clustering approach for wireless sensor networks. In M. Sherif, A. Mellouk, J. Li & P. Bellavista (Eds.), Ad Hoc Networks. ADHOCNETS 2013. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (Vol. 129). Cham: Springer

  16. Meenakshi, D., & Kumar, S. (2012). Energy efficient hierarchical clustering routing protocol for wireless sensor networks. N. Meghanathan, N. Chaki & D. Nagamalai (Eds.), Advances in computer science and information technology. networks and communications. CCSIT 2012. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (Vol. 84). Berlin: Springer.

  17. Neamatollahi, P., Taheri, H., & Naghibzadeh, M. (2011). DESC: Distributed energy efficient scheme to cluster wireless sensor networks. International Federation for Information Processing, 17, 234–246.

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  21. Rajalakshmi, M. C., & Gnana Prakash, A. P. (2014). Energy optimization for large scale wireless sensor network using real-time dynamics. International Journal of Computer Applications, 108(7), 40–46.

    Article  Google Scholar 

  22. Rajalakshmi, M. C., & Gnana Prakash, A. P. (2015). MLO: Multi-level optimization to enhance the network lifetime in large scale WSN. In N. Shetty, N. Prasad, N. Nalini (Eds.), Emerging research in computing, information, communication and applications. New Delhi: Springer.

Download references

Author information

Authors and Affiliations

  1. University of Mysore, Mysore, India

    M. C. Rajalakshmi

  2. Department of Studies in Physics, University of Mysore, Mysore, India

    A. P. Gnana Prakash

Authors
  1. M. C. Rajalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. P. Gnana Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. C. Rajalakshmi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajalakshmi, M.C., Gnana Prakash, A.P. MeMLO: Mobility-Enabled Multi-level Optimization Sensor Network. Wireless Pers Commun 97, 5675–5689 (2017). https://doi.org/10.1007/s11277-017-4802-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4802-2

Keywords

Search

Navigation