Abstract
With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.
Similar content being viewed by others
References
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.
Anisi, M. H., Abdul-Salaam, G., Idris, M. Y. I., Wahab, A. W. A., & Ahmedy, I. (2017). Energy harvesting and battery power based routing in wireless sensor networks. Wireless Networks., 23, 249–266.
Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications, Surveys & Tutorials, 15(2), 551–591.
Halawani, S., & Khan, A. W. (2010). Sensors lifetime enhancement techniques in wireless sensor networks—A survey. Journal of Computing, 2(5), 34–47.
Memon I., & et al. (2019). Smart intelligent system for mobile travelers based on fuzzy logic in IoT communication technology. In International conference on intelligent technologies and applications (pp 22–31).
Memon, I., & Mirza, H. T. (2018). MADPTM: Mix zones and dynamic pseudonym trust management system for location privacy. International Journal of Communication Systems, 31(17), e3795.
Memon, I. (2015). A secure and efficient communication scheme with authenticated key establishment protocol for road networks. Wireless Personal Communications, 85(3), 1167–1191.
Purkar, S. V., & Deshpande, R. S. (2018). Energy efficient clustering protocol to enhance performance of heterogeneous wireless sensor network: EECPEP-HWSN. Journal of Computer Networks and Communications. https://doi.org/10.1155/2018/2078627.
Liang, Q., & Mendel, J. M. (2000). Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems, 8(5), 535–550.
Hwang, J. H., Kwak, H. J., & Park, G. T. (2011). Adaptive intervaltype-2 fuzzy sliding mode control for unknown chaotic system. Nonlinear Dynamics, 63(3), 491–502.
Salgotra, R., Singh, U., & Saha, S. (2018). New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Systems with Applications, 95, 384–420.
Heinzelman, W. B., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of 33rd annual Hawaii international conference on system sciences (HICSS-33). IEEE (p. 223). https://doi.org/10.1109/hicss.2000.926982.
Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings of the IEEE AEROSPACE CONFERENCE, Big Sky, MT, USA, 9–16 March 2002 (vol. 3, pp. 1125–1130).
Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 2004(3), 366–379.
Li, C., Ye, M., Chen, G., & Wu, J. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In Proceedings of the IEEE international conference on mobile adhoc and sensor systems, Washington, DC, USA, 7–10 November 2005 (pp. 598–604).
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670. https://doi.org/10.1109/TWC.2002.804190.
Tripathi, M., Battula, R. B., Gaur, M. S., & Laxmi, V. (2013). Energy efficient clustered routing for wireless sensor network. In Proceedings of the 2013 IEEE 9th international conference on mobile ad hoc and sensor networks, Dalian, China, 11–13 December 2013 (pp. 330–335).
Mechta, D., Harous, S., Alem, I., & Khebbab, D. (2014). LEACH-CKM: Low energy adaptive clustering hierarchy protocol with K-means and MTE. In Proceedings of the 2014 10th international conference on innovations in information technology (IIT), Al Ain, UAE, 9–11 November 2014 (pp. 99–103).
Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In 15th international parallel and distributed processing symposium (IPDPS’01) Workshops, USA, California (pp. 2009–2015).
Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In International parallel and distributed processing symposium, Florida (pp. 195–202).
Aderohunmu, F. A., & Deng, J. D. (2009). An enhanced stable election protocol (E-SEP) for clustered heterogeneous WSN, Department of Information Science, University of Otago, Dunedin 9054, New Zealand.
Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In Proceedings of international workshop on SANPA. http://open.bu.edu/xmlui/bitstream/handle/2144/1548/2004-022-sep.pdf?sequence=1.
Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399. https://doi.org/10.1109/LCOMM.2012.073112.120450.
Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189–199.
Tarhani, M., Kavian, Y. S., & Siavoshi, S. (2014). SEECH: Scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sensors Journal, 14(11), 3944–3954. https://doi.org/10.1109/JSEN.2014.2358567.
Mittal, N., & Singh, U. (2015). Distance-based residual energy-efficient stable election protocol for WSNs. Arabian Journal of Science and Engineering, 40(6), 1637–1646. https://doi.org/10.1007/s13369-015-1641-x.
Mittal, N., Singh, U., & Sohi, B. S. (2017). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks, 23(6), 1809–1821. https://doi.org/10.1007/s11276-016-1255-6.
Adnan, Md A, Razzaque, M. A., Ahmed, I., & Isnin, I. F. (2014). Bio-mimic optimization strategies in wireless sensor networks: A survey. Sensors, 14, 299–345. https://doi.org/10.3390/s140100299.
Hussain, S., & Matin, A. W. (2006). Hierarchical cluster-based routing in wireless sensor networks. In IEEE/ACM international conference on information processing in sensor networks, IPSN.
Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12, 1950–1957. https://doi.org/10.1016/j.asoc.2011.04.007.
Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2011.06.004.
Khalil, E. A., & Attea, B. A. (2013). Stable-aware evolutionary routing protocol for wireless sensor networks. Wireless Personal Communications, 69(4), 1799–1817.
Mittal, N., Singh, U., & Sohi, B. S. (2017). A novel energy efficient stable clustering approach for wireless sensor networks. Wireless Personal Communications, 95, 2947–2971.
Mittal, N., Singh, U., & Sohi, B. S. (2017). Harmony search algorithm based threshold-sensitive energy-efficient clustering protocols for WSNs. Ad Hoc & Sensor Wireless Networks, 36, 149–174.
Mittal, N., Singh, U., Salgotra, R., & Sohi, B. S. (2018). A Boolean spider monkey optimization based energy efficient clustering approach for WSNs. Wireless Networks, 24(6), 2093–2109.
Mittal, N., Singh, U., Sohi, B. S. (2018). An energy aware cluster-based stable protocol for wireless sensor networks. In Neural computing and applications (NCAA) (pp 1–18).
Mittal N., Singh U., Salgotra R., & Bansal M. (2019) An energy efficient stable clustering approach using fuzzy enhanced flower pollination algorithm for WSNs. Neural computing and applications (NCAA) (pp 1–25). https://doi.org/10.1007/s00521-019-04251-4.
Mittal N., Singh U., Sohi B. S. (2016). Modified grey wolf optimizer for global engineering optimization. Applied Computational Intelligence and Soft Computing 1–13.
Kim J. M., Park S. H., Han Y. J., Chung T. M. (2008). CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In 10th international conference on advanced communication technology (Vol. 1, pp. 654–659).
Ran, G., Zhang, H., & Gong, S. (2010). Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information & Computational Science, 7, 767–775.
Lee, J. S., & Cheng, W. L. (2012). Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 12, 2891–2897.
Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13, 1741–1749.
Sert, S. A., Bagci, H., & Yazici, A. (2015). MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.
Kumar G. S., Vinu P. M., & Jacob K. P. (2008). Mobility metric based leach-mobile protocol. In 16th International conference on advanced computing and communications (pp. 248–253).
Wang, W., Du, F., & Xu, Q. (2009). An improvement of LEACH routing protocol based on trust for wireless sensor networks. In 5th international conference on wireless communications, networking and mobile computing (pp. 1–4).
Liu, B., & Wu, Y. (2015). A secure and energy-balanced routing scheme for mobile wireless sensor network. Wireless Sensor Network, 7(11), 137.
Chen, Z., He, M., Liang, W., & Chen, K. (2015). Trust-aware and low energy consumption security topology protocol of wireless sensor network. Journal of Sensors. https://doi.org/10.1155/2015/716468.
Sandhya R., & Sengottaiyan N. (2016). S-SEECH secured-scalable energy efficient clustering hierarchy protocol for wireless sensor network. In International conference on data mining and advanced computing (SAPIENCE) (pp. 306–309).
Rehman, E., Sher, M., Naqvi, S. H. A., Badar, Khan K., & Ullah, K. (2017). Energy efficient secure trust based clustering algorithm for mobile wireless sensor network. Journal of Computer Networks and Communications. https://doi.org/10.1155/2017/1630673.
Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In World congress on Nature and biologically inspired computing, 2009 (pp. 210–214). NaBIC 2009. IEEE.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.
Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82–102.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning–1. Information Sciences, 8, 199–249.
Arain, Q. A., et al. (2016). Clustering based energy efficient and communication protocol for multiple mix-zones over road networks. Wireless Personal Communications, 95(2), 411–428.
Mittal, N. (2018). Moth flame optimization based energy efficient stable clustered routing approach for wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-018-6043-4.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mittal, N., Singh, S., Singh, U. et al. Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks. Wireless Netw 27, 151–174 (2021). https://doi.org/10.1007/s11276-020-02438-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-020-02438-5