Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

eeTMFO/GA: a secure and energy efficient cluster head selection in wireless sensor networks

  • 15 Accesses


Proliferation of technologies in wireless sensor networks is grabbing huge attention across scientific community due to its vast coverage in real life applications. It has emerged as an important technology with lots of potential as it provides useful information to the end users about a target region through real time sensing. Wireless sensor networks due to their characteristics like infrastructure-less deployment, resource restricted nature introduce several issues which may affect the performance of the system. Among these issues, most challenging issues such as energy efficiency, appropriate cluster head selection, secure data delivery and network lifetime enhancement require important concern for enhancement of WSNs which is still herculean task. This paper introduces a secure and energy aware clustering algorithm named energy efficient trusted moth flame optimization and genetic algorithm based clustering algorithm (eeTMFO/GA). Selection of most deserving trustworthy head node (also known as cluster head) is done by using moth flame optimization in clustered WSN framework. In eeTMFO/GA, the fitness function is evaluated on the basis of five important parameters including direct trust metrics such as packet forwarding progress, residual energy of elected node, connected node density, average cluster distance and average delay of transmission. Simulation outcomes have shown significant improvement in energy conservation and network stability period enhancement for eeTMFO/GA in comparison to the existing clustering schemes by 60% in comparison to LEACH protocol, 56.09% when compared to HEED protocol and has shown 42.22% and 16.36% improvement in comparison to ABC and QABC protocols respectively.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. 1.

    Sharma, R., Vashisht, V., Singh, A. V., & Kumar, S. (2018). Analysis of existing clustering algorithms for wireless sensor networks. In System performance and management analytics (pp. 259–277). Retrieved July 31, 2018 from https://link.springer.com/chapter/10.1007/978-981-10-7323-6_22.

  2. 2.

    Sharma, R., Vashisht, V., & Singh, U. (2019). EEFCM-DE: Energy efficient clustering based on fuzzy C means and differential evolution algorithm in wireless sensor networks. IET Communications. https://doi.org/10.1049/iet-com.2018.5546.

  3. 3.

    Hoang, D. C., Yadav, P., Kumar, R., & Panda, S. K. (2010). A robust harmony search algorithm based clustering protocol for wireless sensor networks. In Communications workshops (ICC), 2010 IEEE international conference (pp. 1–5).

  4. 4.

    Song, M. A. O., & Zhao, C. L. (2011). Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. The Journal of China Universities of Posts and Telecommunications,18(6), 89–97.

  5. 5.

    Enami, N., Moghadam, R. A., & Ahmadi, K. D. (2010). A new neural network based energy efficient clustering protocol for wireless sensor networks. In Computer sciences and convergence information technology (ICCIT), 2010 5th international conference (pp. 40–45).

  6. 6.

    Bagci, H., & Yazici, A. (2010). An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In Fuzzy systems (FUZZ), 2010 IEEE international conference (pp. 1–8).

  7. 7.

    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In System sciences, 2000 proceedings of the 33rd annual Hawaii international conference (p. 10).

  8. 8.

    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing,3(4), 366–379.

  9. 9.

    Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. Aerospace Conference Proceedings,3, 1125–1130.

  10. 10.

    Song, F., & Zhao, B. (2008). Trust-based LEACH protocol for wireless sensor networks. In Future generation communication and networking, FGCN’08 second international conference (Vol. 1, pp. 202–207).

  11. 11.

    Shaikh, R. A., Jameel, H., d’Auriol, B. J., Lee, H., Lee, S., & Song, Y. J. (2009). Group-based trust management scheme for clustered wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,20(11), 1698–1712.

  12. 12.

    Guo, W. W., & Looi, M. (2012). A framework of trust-energy balanced procedure for cluster head selection in wireless sensor networks. Journal of Networks,7(10), 1592.

  13. 13.

    Sahoo, R. R., Singh, M., Sardar, A. R., Mohapatra, S., & Sarkar, S. K. (2013). TREE-CR: Trust based secure and energy efficient clustering in WSN. In Emerging trends in computing, communication and nanotechnology (ICE-CCN), 2013 international conference (pp. 532–538).

  14. 14.

    Sahoo, R. R., Singh, M., Sahoo, B. M., Majumder, K., Ray, S., & Sarkar, S. K. (2013). A light weight trust based secure and energy efficient clustering in wireless sensor network: Honey bee mating intelligence approach. Procedia Technology,10, 515–523.

  15. 15.

    Wang, T., Zhang, G., Yang, X., & Vajdi, A. (2016). A trusted and energy efficient approach for cluster-based wireless sensor networks. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2016/3815834.

  16. 16.

    Juliana, R., & Maheswari, P. U. (2016). An energy efficient cluster head selection technique using network trust and swarm intelligence. Wireless Personal Communications,89(2), 351–364.

  17. 17.

    Nimbalkar, N. B., Das, S. S., & Wagh, S. J. (2015). Trust based energy efficient clustering using genetic algorithm in wireless sensor networks (teecga). International Journal of Computer Applications,112(9), 30–33.

  18. 18.

    Tolba, F. D., Ajib, W., & Obaid, A. (2013). Distributed clustering algorithm for mobile wireless sensors networks. In SENSORS (pp. 1–4).

  19. 19.

    Dahane, A., Berrached, N. E., & Loukil, A. (2015). Balanced and safe weighted clustering algorithm for mobile wireless sensor networks. In IFIP international conference on computer science and its applications (pp. 429–441).

  20. 20.

    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, 2017, 1630673.

  21. 21.

    Mittal, N. (2019). Moth flame optimization based energy efficient stable clustered routing approach for wireless sensor networks. Wireless Personal Communications,104(2), 677–694.

  22. 22.

    Kukreja, D., Singh, U., & Reddy, B. V. R. (2012). Analytical models for trust based routing protocols in wireless ad hoc networks. ACM SIGSOFT Software Engineering Notes,37(4), 1–16.

  23. 23.

    Umang, S., Reddy, B. V. R., & Hoda, M. N. (2010). Enhanced intrusion detection system for malicious node detection in ad hoc routing protocols using minimal energy consumption. IET Communications,4(17), 2084–2094.

  24. 24.

    Ahmad, A., Ahmad, S., Rehmani, M. H., & Hassan, N. U. (2015). A survey on radio resource allocation in cognitive radio sensor networks. IEEE Communications Surveys & Tutorials,17(2), 888–917.

  25. 25.

    Talapatra, S., & Roy, A. (2014). Mobility based cluster head selection algorithm for mobile ad-hoc network. International Journal of Computer Network and Information Security,6(7), 42.

  26. 26.

    Ren, M., Khoukhi, L., Labiod, H., Zhang, J., & Veque, V. (2017). A mobility-based scheme for dynamic clustering in vehicular ad-hoc networks (VANETs). Vehicular Communications,9, 233–241.

  27. 27.

    Wang, J., Yin, Y., Zhang, J., Lee, S., & Sherratt, R. S. (2013). Mobility based energy efficient and multi-sink algorithms for consumer home networks. IEEE Transactions on Consumer Electronics,59(1), 77–84.

  28. 28.

    Choudhary, P., Dwivedi, R. K., & Singh, U. (2020). Novel algorithm for leader election process in virtual traffic light protocol. International Journal of Information Technology, 12(1), 113–117.

  29. 29.

    Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems,89, 228–249.

  30. 30.

    Yamany, W., Fawzy, M., Tharwat, A., & Hassanien, A. E. (2015). Moth-flame optimization for training multi-layer perceptrons. In Computer engineering conference (ICENCO), 2015 11th international conference (pp. 267–272).

  31. 31.

    El Aziz, M. A., Ewees, A. A., & Hassanien, A. E. (2017). Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Systems with Applications,83, 242–256.

  32. 32.

    Ewees, A. A., Sahlol, A. T., & Amasha, M. A. (2017). A bio-inspired moth-flame optimization algorithm for Arabic handwritten letter recognition. In Control, artificial intelligence, robotics & optimization (ICCAIRO), 2017 international conference (pp. 154–159).

  33. 33.

    Jangir, N., Pandya, M. H., Trivedi, I. N., Bhesdadiya, R. H., Jangir, P., & Kumar, A. (2016). Moth-flame optimization algorithm for solving real challenging constrained engineering optimization problems. In Electrical, electronics and computer science (SCEECS), 2016 IEEE students’ conference (pp. 1–5).

  34. 34.

    Salleh, M. N. M., Hussain, K., Cheng, S., Shi, Y., Muhammad, A., Ullah, G., & Naseem, R. (2018). Exploration and exploitation measurement in swarm-based metaheuristic algorithms: An empirical analysis. In International conference on soft computing and data mining (pp. 24–32).

  35. 35.

    Holland, J. H. (1992). Genetic algorithms. Scientific American,267(1), 66–73.

  36. 36.

    Tsoulos, I. G. (2009). Solving constrained optimization problems using a novel genetic algorithm. Applied Mathematics and Computation,208(1), 273–283.

  37. 37.

    Herrmann, J. W. (1999). A genetic algorithm for minimax optimization problems. In Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 congress (Vol. 2, pp. 1099–1103).

  38. 38.

    Basagni, S., Carosi, A., & Petrioli, C. (2008). Mobility in wireless sensor networks. In Algorithms and protocols for wireless sensor networks (pp. 267–305).

  39. 39.

    Patel, R., Porwal, V., & Kapoor, R. K. (2014). A review of mobility-based wireless sensor networks. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),3(8), 2811–2815.

  40. 40.

    Kumar, G. S., Vinu, P. M., & Jacob, K. P. (2008). Mobility metric based leach-mobile protocol. In Advanced computing and communications, ADCOM 2008, 16th international conference (pp. 248–253).

  41. 41.

    Wang, H., Chen, Y., & Dong, S. (2016). Research on efficient-efficient routing protocol for WSNs based on improved artificial bee colony algorithm. IET Wireless Sensor Systems,7(1), 15–20.

Download references

Author information

Correspondence to Richa Sharma.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



See Table 1 and Fig. 1.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sharma, R., Vashisht, V. & Singh, U. eeTMFO/GA: a secure and energy efficient cluster head selection in wireless sensor networks. Telecommun Syst (2020). https://doi.org/10.1007/s11235-020-00654-0

Download citation


  • Wireless sensor network
  • Cluster head selection
  • Trust evaluation
  • Moth flame optimization
  • Genetic algorithm