Skip to main content

Advertisement

SpringerLink
Log in

A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

One of the most important issues in wireless sensor networks (WSNs) is to reduce energy consumption and increase the network lifetime. Proper election of cluster head is one of the approaches to reduce energy consumption in the network. Among existing methods, low-energy adaptive cluster hierarchy (LEACH) is the most prevalent routing algorithm, in which the cluster head is elected based on a given threshold. In LEACH algorithm, only the cluster heads are allowed to send information to the base station (BS). In this paper, a novel routing protocol based on super cluster head election using fuzzy logic in three levels (SCHFTL) is proposed, in which a super cluster head is elected among the cluster heads. The super cluster head election is performed based on a fuzzy description in three levels using Mamdani inference engine. Effectiveness of the proposed SCHFTL routing protocol is verified through MATLAB simulations in terms of death, time of the first node and network lifetime compared with LEACH, cluster head election mechanism using fuzzy logic (CHEF) and fuzzy-based master cluster head election leach (F-MCHEL) protocols.

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

Similar content being viewed by others

References

  1. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38:393–422

    Article  Google Scholar 

  2. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 51:2292–2330

    Article  Google Scholar 

  3. Omar M, Yahiaoui S, Bouabdallah A (2016) Reliable and energy aware query-driven routing protocol for wireless sensor networks. Ann Telecommun 71(1):73–85

    Article  Google Scholar 

  4. Espes D, Lagrange X, Suárez L (2015) A cross-layer MAC and routing protocol based on slotted aloha for wireless sensor networks. Ann Telecommun 70(3):159–169

    Article  Google Scholar 

  5. Martirosyan A, Boukerche A, Pazzi RWN (2008) Energy-aware and quality of service-based routing in wireless sensor networks and vehicular ad hoc networks. Ann Telecommun 63(11):669–681

    Article  Google Scholar 

  6. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Hawaii International Conference on System. Sciences:1–10

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

    Article  Google Scholar 

  8. Heinzelman WB, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670

    Article  Google Scholar 

  9. Smaragdakis, G., Matta, I., Bestavros, A. (2004). SEP: a stable election protocol for clustered heterogeneous wireless sensor networks. Second International Workshop on Sensor and Actor Network Protocols and Applications, 251-261

  10. Manzoor B, Javaid N, Rehman O, Akbar M, Nadeem Q, Iqbal A, Ishfaq M (2013) Q-LEACH: a new routing protocol for WSNs. Procedia Computer Science 19:926–931

    Article  Google Scholar 

  11. Nayak P, Devulapalli A (2016) A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors J 16(1):137–144

    Article  Google Scholar 

  12. Jiang CJ, Shi WR, Tang XL, Wang P, Xiang M (2012) Energy balanced unequal clustering routing protocol for wireless sensor networks. Journal of Software 23(5):1222–1232

    Article  Google Scholar 

  13. Mitton N, Fleury E, Lassous IG, Tixeuil S (2005) Self-stabilization in self-organized multihop wireless networks. IEEE International Conference on Distributed Computing Systems Workshops:909–915

  14. Javaid N, Bibi A, Javaid A, Malik SA (2011) Modelling routing overhead generated by wireless proactive routing protocols. IEEE GLOBECOM Workshops:1072–1076

  15. Javaid N, Ullah M, Djouani K (2011) Identifying design requirements for wireless routing link metrics. IEEE Global Telecommunications Conference:1–5

  16. Javaid N, Bibi A, Javaid A, Khan ZA, Latif K, Ishfaq M (2014) Investigating quality routing link metrics in wireless multi-hop networks. Ann Telecommun 69(3):209–217

    Article  Google Scholar 

  17. 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 Telecommunications, 3. Issue 10:8–13

    Google Scholar 

  18. Mostafa B, Saad C, Abderrahmane H (2013) Fuzzy logic approach to improving stable election protocol for clustered heterogeneous wireless sensor networks. Journal of Theoretical and Applied Information Technology 53(3):334–339

    Google Scholar 

  19. Anno J, Barolli L, Durresi A, Xhafa F, Koyama A (2008) Performance evaluation of two fuzzy-based cluster head selection systems for wireless sensor networks. Mob Inf Syst 4(4):297–312

    Google Scholar 

  20. Kim JM, Park SH, Han YJ, Chung TM (2008) CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. International Conference on Advanced Communication Technology:654–659

  21. Gajjar S, Sarkar M, Dasgupta K (2014) Cluster head selection protocol using fuzzy logic for wireless sensor networks. International Journal of Computer Applications 97(7):38–43

    Article  Google Scholar 

  22. Seyedkolaei AA, Zakerolhosseini A (2014) CFMTL: clustering wireless sensor network using fuzzy logic and mobile sink in three-level. Advances in Computer Science: An International Journal 3(6):23–28

    Google Scholar 

  23. Wu J, Zhang L, Bai Y, Sun Y (2015) Cluster-based consensus time synchronization for wireless sensor networks. IEEE Sensors J 15(3):1404–1413

    Article  Google Scholar 

  24. Bagci H, Yazici A (2010) An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In: IEEE International Conference on Fuzzy Systems, pp 1741–1749

    Google Scholar 

  25. Zhou H, Ying H (2013) A method for deriving the analytical structure of a broad class of typical interval type-2 Mamdani fuzzy controllers. IEEE Trans Fuzzy Syst 21(3):447–458

    Article  Google Scholar 

  26. Suh B, Berber S (2013) Asynchronous data-forwarding strategy to reduce forwarding delay in energy-harvesting wireless sensor networks. Electron Lett 49(7):1492–1494

    Article  Google Scholar 

  27. Angrisani L, Ventre G, Peluso L, Tedesco A (2006) Measurement of processing and queuing delays introduced by an open-source router in a single-hop network. IEEE Trans Instrum Meas 55(4):1065–1076

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Moosa Ayati

  2. Advanced Instrumentation Labratuary, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohammad Hossein Ghayyoumi

  3. Department of Power Grid Control Systems, Niroo Research Institute (NRI), Tehran, Iran

    Atiyeh Keshavarz-Mohammadiyan

Authors
  1. Moosa Ayati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hossein Ghayyoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atiyeh Keshavarz-Mohammadiyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atiyeh Keshavarz-Mohammadiyan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayati, M., Ghayyoumi, M.H. & Keshavarz-Mohammadiyan, A. A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks. Ann. Telecommun. 73, 535–546 (2018). https://doi.org/10.1007/s12243-018-0631-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-018-0631-x

Keywords

Search

Navigation