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.
Similar content being viewed by others
References
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.
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.
Gubbi, J., et al. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
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.
Jing, Q., et al. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.
Whitmore, A., et al. (2015). The internet of things—A survey of topics and trends. Information Systems Frontiers, 17(2), 261–274.
Yan, Z., et al. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42(2014), 120–134.
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.
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.
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.
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.
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.
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.
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.
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.
Saleem, M., et al. (2012). BeeSensor: An energy-efficient and scalable routing protocol for wireless sensor networks. Information Sciences, 200, 38–56.
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.
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.
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.
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.
Dvir, A., & Vasilakos, A. (2010). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 40(4), 405.
Vasilakos, A., et al. (2011). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.
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.
Vasilakos, A., et al. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52(2015), 1–10.
Yang, M., et al. (2014). Software-defined and virtualized future mobile and wireless networks: A survey. Mobile Networks and Applications., 20(1), 4–18.
Sindhwani, N., & Vaid, R. (2013). VLEACH: An energy efficient communication protocol for WSN. Mechanica Confab, 2(2), 79–84.
Zeng, Y., et al. (2012). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.
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.
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.
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.
Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC. doi:10.1109/TC.2015.2417543.
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.
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.
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.
Liu, X., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.
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.
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.
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.
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.
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.
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.
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.
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.
Puneet, A., & Sharma, V. (2013). Cluster head selection in wireless sensor networks under fuzzy environment. ISRN Sensor Networks. doi:10.1155/2013/909086.
AbdulAlim, M. A., et al. (2013). A fuzzy based clustering protocol for energy-efficient wireless sensor networks. Advanced Materials Research, 760–762, 685–690.
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.
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.
Heinzelman, W., et al. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-016-1266-3