Skip to main content

Advertisement

Log in

Two-step fuzzy logic system to achieve energy efficiency and prolonging the lifetime of WSNs

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Mainly because of resource restrictions in wireless sensor networks (WSNs), extending the lifetime of the network, has gained significant attention in the last several years. As energy becomes a quite challenging issue in these networks, clustering protocols are employed to deal with this problem. One of the main research areas in cluster-based routing protocols is fair distribution and balancing the overall energy consumption in the WSN, by selecting the most suitable cluster heads (CHs). In order to reduce the energy consumption and enhancing the CHs selection process a new routing protocol based on fuzzy logic has been proposed. There exist several algorithms based of fuzzy logic to select the most proper CHs for the network. But these algorithms do not consider all the important parameters and information of the sensor nodes in order to guarantee the optimal selection of the CHs. In The proposed algorithm, a two-step fuzzy logic system is used to select the appropriate CHs. The selection of CHs is based on six descriptors; residual energy, density, distance to base station, vulnerability index, centrality and distance between CHs. The result of the simulation indicates that, the proposed algorithm performs better comparing with some other similar approaches in case of fair distribution and balancing of the overall energy consumption.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Arampatzis, T., et al. (2005). A survey of applications of wireless sensors and wireless sensor networks. In Proceedings of the 2005 IEEE International Symposium on Intelligent Control, 2005, Mediterranean Conference on Control and Automation (pp. 719–724), 27–29 June 2005.

  2. Qin, Y., et al. (2016). When things matter: A survey on data-centric internet of things. Journal of Network and Computer Applications, 64(2016), 137–153.

    Article  Google Scholar 

  3. Gubbi, J., et al. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

    Article  Google Scholar 

  4. Sheng, Z., et al. (2013). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98.

    Article  Google Scholar 

  5. Jing, Q., et al. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  6. Whitmore, A., et al. (2015). The internet of things—A survey of topics and trends. Information Systems Frontiers, 17(2), 261–274.

    Article  Google Scholar 

  7. Yan, Z., et al. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42(2014), 120–134.

    Article  Google Scholar 

  8. Li, M., et al. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557. doi:10.1109/JPROC.2013.2257631.

    Article  Google Scholar 

  9. Zhang, X. M., et al. (2015). Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754. doi:10.1109/TMC.2014.2331966.

    Article  MathSciNet  Google Scholar 

  10. Zhu, N., & Vasilakos, A. V. (2015). A generic framework for energy evaluation on wireless sensor networks. Wireless Networks. doi:10.1007/s11276-015-1033-x.

  11. Tamandani, Y. K., & Bokhari, M. U. (2015). The impact of sink location on the performance, throughput and energy efficiency of the WSNs. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), Noida (pp. 1–5). doi:10.1109/ICRITO.2015.7359244.

  12. Yao, Y., et al. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.

    Article  Google Scholar 

  13. Chilamkurti, N., et al. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors. Article ID 134165. doi:10.1155/2009/134165.

  14. Liu, Y., et al. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816. doi:10.1049/iet-com.2009.0164.

    Article  Google Scholar 

  15. Li, P., et al. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273. doi:10.1109/TPDS.2013.2297105.

    Article  Google Scholar 

  16. Saleem, M., et al. (2012). BeeSensor: An energy-efficient and scalable routing protocol for wireless sensor networks. Information Sciences, 200, 38–56.

    Article  Google Scholar 

  17. Bhuiyan, M. Z. A., et al. (2015). Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Transactions on Computers, 64(7), 1968–1982. doi:10.1109/TC.2014.2346209.

    Article  MathSciNet  MATH  Google Scholar 

  18. Tamandani, Y. K., & Bokhari, M. U. (2016). SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network. Wireless Networks, 22(2), 647–653.

    Article  Google Scholar 

  19. Heinzelman, W., et al. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000. IEEE.

  20. Xiao, Y., et al. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554. doi:10.1109/TMC.2011.190.

    Article  Google Scholar 

  21. Dvir, A., & Vasilakos, A. (2010). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 40(4), 405.

    Article  Google Scholar 

  22. Vasilakos, A., et al. (2011). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.

    Google Scholar 

  23. Lopez-Perez, D., et al. (2013). On distributed and coordinated resource allocation for interference mitigation in self-organizing LTE networks. IEEE/ACM Transactions on Networking, 21(4), 1145–1158.

    Article  Google Scholar 

  24. Vasilakos, A., et al. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52(2015), 1–10.

    Article  Google Scholar 

  25. Yang, M., et al. (2014). Software-defined and virtualized future mobile and wireless networks: A survey. Mobile Networks and Applications., 20(1), 4–18.

    Article  Google Scholar 

  26. Sindhwani, N., & Vaid, R. (2013). VLEACH: An energy efficient communication protocol for WSN. Mechanica Confab, 2(2), 79–84.

    Google Scholar 

  27. Zeng, Y., et al. (2012). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  28. Mehmood, A., et al. (2015). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972–989.

    Article  Google Scholar 

  29. Han, K., et al. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113. doi:10.1109/MCOM.2013.6553686.

    Article  Google Scholar 

  30. Ganesh, S., & Amutha, R. (2013). Efficient and secure routing protocol for wireless sensor networks through SNR based dynamic clustering mechanisms. Journal of Communications and Networks, 15(4), 422–429.

    Article  Google Scholar 

  31. Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC. doi:10.1109/TC.2015.2417543.

    MATH  Google Scholar 

  32. Xiang, L., Luo, J., & Rosenberg, C. (2013). Compressed data aggregation: Energy-efficient and high-fidelity data collection. IEEE/ACM Transactions on Networking (TON), 21(6), 1722–1735.

    Article  Google Scholar 

  33. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823. doi:10.1109/TNET.2014.2306592.

    Article  Google Scholar 

  34. Martinez-de, D., et al. (2013). Cooperation between uas and wireless sensor networks for efficient data collection in large environments. Journal of Intelligent and Robotic Systems, 70(1-4), 491–508.

    Google Scholar 

  35. Liu, X., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.

    Article  Google Scholar 

  36. Xiang, L., et al. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Salt Lake City, UT (pp. 46–54). doi:10.1109/SAHCN.2011.5984932.

  37. Wei, G., et al. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.

    Article  MathSciNet  Google Scholar 

  38. Prabhavathi, S., et al. (2016). Energy efficient dynamic reconfiguration of routing agents for WSN data aggregation. In N. R. Shetty, N. H. Prasad, & N. Nalini (Eds.), Emerging research in computing, information, communication and applications (pp. 291–301). Springer India.

  39. Sengupta, S., et al. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1093–1102.

    Article  Google Scholar 

  40. Yuning, S., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430. doi:10.1109/TNSM.2014.2346080.

    Article  Google Scholar 

  41. Liu, L., et al. (2015). Physarum optimization: A biology-inspired algorithm for the Steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 818–831. doi:10.1109/TC.2013.229.

    Article  MathSciNet  Google Scholar 

  42. Acampora, G., et al. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems, 5(2), 1–26.

    Article  Google Scholar 

  43. Gupta, I., et al. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Communication Networks and Services Research Conference, 2005. Proceedings of the 3rd Annual. IEEE.

  44. Puneet, A., & Sharma, V. (2013). Cluster head selection in wireless sensor networks under fuzzy environment. ISRN Sensor Networks. doi:10.1155/2013/909086.

  45. AbdulAlim, M. A., et al. (2013). A fuzzy based clustering protocol for energy-efficient wireless sensor networks. Advanced Materials Research, 760–762, 685–690.

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Kim, J., et al. (2008). CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In 10th International Conference on Advanced Communication Technology, 2008. ICACT 2008 (Vol. 1). IEEE.

  48. Heinzelman, W., et al. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yahya Kord Tamandani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tamandani, Y.K., Bokhari, M.U. & Shallal, Q.M. Two-step fuzzy logic system to achieve energy efficiency and prolonging the lifetime of WSNs. Wireless Netw 23, 1889–1899 (2017). https://doi.org/10.1007/s11276-016-1266-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-016-1266-3

Keywords

Navigation