Abstract
Wireless sensor networks is one of the important parts in modern-day communication that employing low-cost sensor devices with different environmental and physical parameters. The communication path between the base station and sensor nodes are built with the help of an efficient routing protocol. In the past years, the existing protocols met few difficulties in terms of higher computational complexity, poor cluster head selection performance, higher energy consumption, expensive in cluster head selection, scalability management, and uneven load distribution, and so on. In this paper, we proposed BM-BWO with fuzzy logic based HEED protocol (BMBWFL-HEED). In BMBWFL-HEED, we use the combination of the boosted mutation based black widow optimization (BM-BWO) algorithm with HEED protocol to select the higher residual energy. Particularly, the mutation phase of the Black Widow Optimization (BWO) algorithm is improved with the help of direction average strategy (BM-BWO). The fuzzy logic system selects the most relevant and optima cluster heads. Different kinds of experimental analysis, benchmark functions are applied to evaluate the performance of proposed BMBWFL-HEED protocol and it is compared with some existing algorithms like ICFL -HEED, HEED, and ICHB-HEED. In the case of residual energy, a variation of energy consumption and the number of cluster head formation for both homogeneous and heterogeneous environments. The proposed BMBWFL-HEED method demonstrates optimal performance output among all other methods.
Similar content being viewed by others
References
Singh, S., Chand, S., & Kumar, B. (2016). Energy efficient clustering protocol using fuzzy logic for heterogeneous WSNs. Wireless Personal Communications, 86(2), 451–475.
Gupta, P., & Sharma, A. K. (2019). Designing of energy efficient stable clustering protocols based on BFOA for WSNs. Journal of Ambient Intelligence and Humanized Computing, 10(2), 681–700.
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, 32, 1–21.
Ravikumar, S., & Kavitha, D. (2020). IoT based home monitoring system with secure data storage by Keccak–Chaotic sequence in cloud server. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02424-x.
Kavitha, D., & Ravikumar, S. (2021). IOT and context-aware learning-based optimal neural network model for real-time health monitoring. Transactions on Emerging Telecommunications Technologies, 32(1), e4132. https://doi.org/10.1002/ett.4132.
Ravikumar, S., & Kavitha, D. (2021). IOT based autonomous car driver scheme based on ANFIS and black widow optimization. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02725-1.
Kavitha, D., & Ravikumar, S. (2020). Designing an IoT based autonomous vehicle meant for detecting speed bumps and lanes on roads. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02419-8.
Hu, Y., & Niu, Y. (2018). An energy-efficient overlapping clustering protocol in WSNs. Wireless Networks, 24(5), 1775–1791.
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.
Singh, R., & Verma, A. K. (2017). Energy efficient cross layer based adaptive threshold routing protocol for WSN. AEU-International Journal of Electronics and Communications, 72, 166–173.
Bhardwaj, R., & Kumar, D. (2019). MOFPL: Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN. Pervasive and Mobile Computing, 58, 101029.
Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers & Security, 77, 277–288.
Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems, 9(3), 117–126.
Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.
Vinu, S. (2019). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.
Hanaoui, M., Aouami, R., Rifi, M. (2016) Smart antenna system for wireless sensor networks to improve energy efficiency. 5(3).
Devika, B., & Sudha, P. N. (2019). Power optimization in MANET using topology management. Engineering Science and Technology an International Journal, 23, 565–575.
Gupta, P., & Sharma, A. K. (2019). Energy efficient clustering protocol for WSNs based on bio-inspired ICHB algorithm and fuzzy logic system. Evolving Systems, 10(4), 659–677.
Saini, A., Kansal, A., & Randhawa, N. S. (2019). Minimization of energy consumption in WSN using hybrid WECRA approach. Procedia Computer Science, 155, 803–808.
Vinitha, A., & Rukmini, M. S. S. (2019). “Secure and energy aware multi-hop routing protocol in WSN using Taylor-based hybrid optimization algorithm. Journal of King Saud University-Computer and Information Sciences.
Allam, A. H., Taha, M., & Zayed, H. H. (2019) Enhanced zone-based energy aware data collection protocol for WSNs (E-ZEAL). Journal of King Saud University-Computer and Information Sciences
Anand, M., & Sasikala, T. (2019). Efficient energy optimization in mobile ad hoc network (MANET) using better-quality AODV protocol. Cluster Computing, 22(5), 12681–12687.
Chaudhry, R., & Tapaswi, S. (2018). Optimized power control and efficient energy conservation for topology management of MANET with an adaptive Gabriel graph. Computers and Electrical Engineering, 72, 1021–1036.
Yu, J., Wang, G., & Gu, X. (2014). An energy-aware distributed unequal clustering protocol for wireless sensor networks. International Journal of Distributed Sensor Networks, 20, 8.
Park, G. Y., Kim, H., Jeong, H. W., & Youn, H. Y. (2013) A novel cluster head selection method based on k-means algorithm for energy efficient wireless sensor network. In Proceedings of the 27th international conference on advanced information networking and applications workshops (WAINA '13), pp. 910–915.
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 Proceedings of the 10th International Conference on Advanced Communication Technology (ICACT '08), pp. 654–659
Qing, Li., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.
Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667.
Antonialli-Junior, W. F., & Guimarães, I. (2014). Aggregation behavior in spiderlings: a strategy for increasing life expectancy in Latrodectus geometricus (Araneae: Theridiidae). Sociobiology, 59(2), 463–475.
Hayyolalam, V., & Kazem, A. A. P. (2020). Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249.
Yang, X., Li, J., & Peng, X. (2019). An improved differential evolution algorithm for learning high-fidelity quantum controls. Science Bulletin, 64(19), 1402–1408.
Rejeesh, M. R. (2019). Interest point based face recognition using adaptive neuro fuzzy inference system. Multimedia Tools Applications, 78(16), 22691–22710.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2020). “Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, pp. 10
Heinzelman, W. A., Chandrakasan, P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Shi. (2001). Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation, vol. 1, pp. 81–86.
Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217(7), 3166–3173.
Kaur, S., Awasthi, L., Sangal, A., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
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
Sheriba, S.T., Rajesh, D.H. Energy-efficient clustering protocol for WSN based on improved black widow optimization and fuzzy logic. Telecommun Syst 77, 213–230 (2021). https://doi.org/10.1007/s11235-021-00751-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11235-021-00751-8