Cluster Computing

, Volume 22, Supplement 1, pp 1361–1372 | Cite as

Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things

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


With ample technological developments and flourishing demand for digital assistance in daily life and work environments that goes along with it, technologies are required to derive this application domain to the subsequent level. Internet of Things (IoT) is considered as one of vision for such technologies. This paper intends to develop self adaptive whale optimization algorithm (SAWOA) for the accomplishment of energy-aware cluster head selection and clustering protocols under wireless sensor network (WSN)—based IoT. Along with the parameters like energy, distance, and delay of sensor nodes in WSN, this simulation considers both load and temperature of IloT devices. After modeling the simulation, it carries out a valuable performance analysis in terms of network efficiency, normalized energy and load and temperature of the selected cluster head. The performance analysis compares the effectiveness of proposed SAWOA over traditional artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), adaptive GSA (AGSA) and WOA-based cluster head selection models. The outcome from simulation model proves the successful performance of SAWOA in cluster head selection so that the lifetime of network is prolonged.


WSN IoT Cluster head selection WOA SAWOA 


  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Dai, H., Xu, H.: Key predistribution approach in wireless sensor networks using LU matrix. IEEE Sens. J. 10(8), 1399–1409 (2010)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Park, H., Kim, H., Joo, H., Song, J.S.: Recent advancements in the Internet-of-Things related standards: a oneM2M perspective. ICT Express 2(3), 126–129 (2016)CrossRefGoogle Scholar
  8. 8.
    Ashraf, Q.M., Qazi, M.H.: Mohamed Hadi: autonomic schemes for threat mitigation in Internet of Things. J. Netw. Comput. Appl. 49, 112–127 (2015)CrossRefGoogle Scholar
  9. 9.
    Perera, C., Vasilakos, A.V.: A knowledge-based resource discovery for Internet of Things. Knowl. Based Syst. 109, 122–136 (2016)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Zhong, S., Wu, F.: A collusion-resistant routing scheme for noncooperative wireless Ad Hoc networks. IEEE/ACM Trans. Network. 18(2), 582–595 (2010)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    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
  19. 19.
    Cavalcante, E., Pereira, J., Alves, M.P., Marcelo, 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
  20. 20.
    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
  21. 21.
    Raza, S., Misra, P., He, Z., Voigt, T.: Building the Internet of Things with bluetooth smart. Ad Hoc Netw. 57, 19–31 (2017)CrossRefGoogle Scholar
  22. 22.
    Luo, S., Ren, B.: The monitoring and managing application of cloud computing based on Internet of Things. Comput. Methods Progr. Biomed. 130, 154–161 (2016)CrossRefGoogle Scholar
  23. 23.
    Sivieri, A., Mottolaa, L., Cugola, G.: Building Internet of Things software with ELIoT. Comput. Commun. 89–90, 141–153 (2016)CrossRefGoogle Scholar
  24. 24.
    Karkouch, A., Mousannif, H.: Data quality in internet of things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57–81 (2016)CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    Zhou, Z., Yao, B., Xing, R., Shu, L., Bu, S.: 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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)CrossRefzbMATHGoogle Scholar
  31. 31.
    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
  32. 32.
    Praveen, M., Reddy, K.: Energy efficient cluster head selection for internet of things. New Rev. Inf. Netw. 22(1), 54–70 (2017)CrossRefGoogle Scholar
  33. 33.
    Frye, L., Cheng, L., Du, S., Bigrigg, M.W.: Topology maintenance of wireless sensor networks in node failure-prone environments. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, FL, pp. 886–891, (2006)Google Scholar
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    Mirjalili, S., Lewis, A., Algorithm, T.W.O.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

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