Cluster Computing

, Volume 22, Supplement 6, pp 13095–13107 | Cite as

A hybrid cluster head selection model for Internet of Things

  • M. Praveen Kumar ReddyEmail author
  • M. Rajasekhara Babu


Internet of Things (IoT) is one of the rising networking standards that gap between the physical world and the cyber. Energy conservation of IoT devices becomes a fundamental challenge for extending the life time of the network. As a solution to this challenge, cluster head selection can be used. This paper intends to adopt a hybrid model with both Moth Flame Optimization and Ant Lion Optimization (ALO) to improve the performance of cluster head selection among IoT devices in WSN–IoT network. The particular simulation approach not only preserves energy of the sensor node by maintaining distance and delay but also balances the temperature and load of IoT devices for attaining the optimal cluster head selection in WSN–IoT network. Further, it compares the performance of the proposed hybrid model over the traditional models like Artificial Bee Colony, Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, ALO, MFO and Adaptive GSA. The simulation analysis considers the convergence, sustainability of alive nodes, normalized energy, load, and temperature. Thus the proposed simulation results are more efficient for prolonging the life time of the network.


IoT devices Cluster head selection MFO ALO Hybrid model 


  1. 1.
    Duan, J., Gao, D., Yang, D., Foh, C.H., Chen, H.H.: An energy-aware trust derivation scheme with game theoretic approach in wireless sensor networks for IoT applications. IEEE Internet Things J. 1(1), 58–69 (2014)CrossRefGoogle Scholar
  2. 2.
    Zhou, Z., Yao, B., Xing, R., Shu, L.: E-CARP: An energy efficient routing protocol for UWSNs in the Internet of underwater things. IEEE Sens. J. 16(11), 4072–4082 (2016)CrossRefGoogle Scholar
  3. 3.
    Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., Tolba, A.: ERGID: an efficient routing protocol for emergency response Internet of Things. J. Netw. Comput. Appl. 72, 104–112 (2016)CrossRefGoogle Scholar
  4. 4.
    Lee, I.-G., Kim, M.: Interference-aware self-optimizing Wi-Fi for high efficiency Internet of Things in dense networks. Comput. Commun. 89–90(1), 60–74 (2016)Google Scholar
  5. 5.
    Qiu, T., Luo, D., Xia, F., Deonauth, N., Si, W., Tolba, A.: A greedy model with small world for improving the robustness of heterogeneous Internet of Things. Comput. Netw. 101, 127–143 (2016)CrossRefGoogle Scholar
  6. 6.
    Moosavi, S.R., Gia, T.N., Nigussie, E., Rahmani, A.M., Virtanen, S., Tenhunen, H., Isoaho, J.: End-to-end security scheme for mobility enabled healthcare Internet of Things. Future Gener. Comput. Syst. 64, 108–124 (2016)CrossRefGoogle Scholar
  7. 7.
    Di Marco, P., Athanasiou, G., Mekikis, P.-V., Fischione, C.: MAC-aware routing metrics for the internet of things. Comput. Commun. 74(15), 77–86 (2016)CrossRefGoogle Scholar
  8. 8.
    Frye, L., Cheng, L., Du, S., Bigrigg, M.W.: Topology maintenance of wireless sensor networks in node failure-prone environments. In: 2006 IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, pp. 886–891 (2006)Google Scholar
  9. 9.
    Ashraf, Q.M., Habaebi, M.H.: Autonomic schemes for threat mitigation in Internet of Things. J. Netw. Comput. Appl. 49, 112–127 (2015)CrossRefGoogle Scholar
  10. 10.
    Perera, C., Vasilakos, A.V.: A knowledge-based resource discovery for Internet of Things. Knowl. Based Syst. 109, 122–136 (2016)CrossRefGoogle Scholar
  11. 11.
    Dai, H., Xu, H.: Key predistribution approach in wireless sensor networks using LU matrix. IEEE Sens. J. 10(8), 1399–1409 (2010)CrossRefGoogle Scholar
  12. 12.
    Abusalah, L., Khokhar, A., Guizani, M.: A survey of secure mobile ad hoc routing protocols. IEEE Commun. Surv. Tutor. 10(4), 78–93 (2008)CrossRefGoogle Scholar
  13. 13.
    Zhong, S., Wu, F.: A collusion-resistant routing scheme for noncooperative wireless ad hoc networks. IEEE/ACM Trans. Netw. 18(2), 582–595 (2010)CrossRefGoogle Scholar
  14. 14.
    Li, C.Z., Hong, J., Xue, F., Shen, G.Q., Xu, X., Luo, L.: SWOT analysis and Internet of Things-enabled platform for prefabrication housing production in Hong Kong. Inf. Syst. 62, 29–41 (2016)CrossRefGoogle Scholar
  15. 15.
    Li, Z., Chen, R., Liu, L., Min, G.: Dynamic resource discovery based on preference and movement pattern similarity for large-scale social Internet of Things. IEEE Internet Things J. 3(4), 581–589 (2016)CrossRefGoogle Scholar
  16. 16.
    Wu, D., Bao, L., Liu, C.H.: Scalable channel allocation and access scheduling for wireless internet-of-things. IEEE Sens. J. 13(10), 3596–3604 (2013)CrossRefGoogle Scholar
  17. 17.
    Yachir, A., Amirat, Y., Chibani, A., Badache, N.: Event-aware framework for dynamic services discovery and selection in the context of ambient intelligence and Internet of Things. IEEE Trans. Autom. Sci. Eng. 13(1), 85–102 (2016)CrossRefGoogle Scholar
  18. 18.
    Zhang, D., Yang, L.T., Chen, M., Zhao, S., Guo, M., Zhang, Y.: Real-time locating systems using active RFID for Internet of Things. IEEE Syst. J. 10(3), 1226–1235 (2016)CrossRefGoogle Scholar
  19. 19.
    Kawamoto, Y., Nishiyama, H., Fadlullah, Z.M., Kato, N.: Effective data collection via satellite-routed sensor system (SRSS) to realize global-scaled Internet of Things. IEEE Sens. J. 13(10), 3645–3654 (2013)CrossRefGoogle Scholar
  20. 20.
    Kougianos, E., Mohanty, S.P., Coelho, G., Albalawi, U., Sundaravadivel, P.: Design of a high-performance system for secure image communication in the Internet of Things. IEEE Access 4, 1222–1242 (2016)CrossRefGoogle Scholar
  21. 21.
    Luo, S., Ren, B.: The monitoring and managing application of cloud computing based on Internet of Things. Comput. Methods Prog. Biomed. 130, 154–161 (2016)CrossRefGoogle Scholar
  22. 22.
    Liu, Y., Han, W., Zhang, Y., Li, L., Wang, J., Zheng, L.: An Internet-of-Things solution for food safety and quality control: a pilot project in China. J. Ind. Inf. Integr. 3, 1–7 (2016)Google Scholar
  23. 23.
    Park, H., Kim, H., Joo, H., Song, J.: Recent advancements in the Internet-of-Things related standards: a oneM2M perspective. ICT Express, September 2016CrossRefGoogle Scholar
  24. 24.
    Sivieri, A., Mottolaa, L., Cugola, G.: Building Internet of Things software with ELIoT. Comput. Commun. 89–90, 141–153 (2016)CrossRefGoogle Scholar
  25. 25.
    Karkouch, A., Mousannif, H., Moatassime, H.A., Noel, T.: Data quality in Internet of Things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57–81 (2016)CrossRefGoogle Scholar
  26. 26.
    Zhu, T., Dhelim, S., Zhou, Z., Yang, S., Ning, H.: An architecture for aggregating information from distributed data nodes for industrial Internet of Things. Comput. Electr. Eng. 58, 337–349 (2016)CrossRefGoogle Scholar
  27. 27.
    Li, F., Han, Y., Jin, C.: Practical access control for sensor networks in the context of the Internet of Things. Comput. Commun. 89–90, 154–164 (2016)CrossRefGoogle Scholar
  28. 28.
    Cavalcante, E., Pereira, J., Alves, M.P., Maia, P., Moura, R., Batista, T., Delicato, F.C., Pires, P.F.: On the interplay of Internet of Things and cloud computing: a systematic mapping study. Comput. Commun. 89–90, 17–33 (2016)CrossRefGoogle Scholar
  29. 29.
    Hsu, C.-L., Lin, J.C.-C.: An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Comput. Hum. Behav. 62, 516–527 (2016)CrossRefGoogle Scholar
  30. 30.
    Raza, S., Misra, P., He, Z., Voigt, T.: Building the Internet of Things with bluetooth smart. Ad Hoc Netw. 57, 19–31 (2016)CrossRefGoogle Scholar
  31. 31.
    Coelho, L.D.S., Mariani, V.C., Tutkun, N., Alotto, P.: Magnetizer design based on a quasi-oppositional gravitational search algorithm. IEEE Trans. Magn. 50(2), 705–708 (2014)CrossRefGoogle Scholar
  32. 32.
    Nadakuditi, G., Sharma, V., Naresh, R.: Application of non-dominated sorting gravitational search algorithm with disruption operator for stochastic multiobjective short term hydrothermal scheduling. IET Gener. Transm. Distrib. 10(4), 862–872 (2016)CrossRefGoogle Scholar
  33. 33.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)CrossRefGoogle Scholar
  34. 34.
    Misra, G., Kumar, V., Agarwal, A., Agarwal, K.: Internet of Things (IoT)–a technological analysis and survey on vision, concepts, challenges, innovation directions, technologies, and applications (an upcoming or future generation computer communication system technology). Am. J. Electr. Electron. Eng. 4(01), 23–32 (2016)CrossRefGoogle Scholar
  35. 35.
    Agarwal, A., Misra, G., Agarwal, K.: The 5th generation mobile wireless networks–key concepts, network architecture and challenges. Am. J. Electr. Electron. Eng. 3(2), 22–28 (2015)Google Scholar
  36. 36.
    Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015)CrossRefGoogle Scholar
  37. 37.
    Ali, E.S., Abd Elazim, S.M., Abdelaziz, A.Y.: Ant lion optimization algorithm for renewable distributed generations. Energy 116, 445–458 (2016)CrossRefGoogle Scholar
  38. 38.
    Praveen Kumar Reddy, M., Rajasekhara Babu, M.: Energy efficient cluster head selection for Internet of Things. New Rev. Inf. Netw. 22(1), 54–70 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • M. Praveen Kumar Reddy
    • 1
    Email author
  • M. Rajasekhara Babu
    • 1
  1. 1.School of Computing Science and EngineeringVIT UniversityVelloreIndia

Personalised recommendations