Abstract
Wireless Sensor Network (WSN) is a network including allocated independence systems called sensor nodes, which are enabled for sensing or monitoring circumferential conditions. One of the main challenges in WSN is the battery lifetime and energy consumption of sensor nodes. Hence, in order to reduce energy consumption it is important to consider the energy consumption in a completely optimal way in WSNs. In this paper, we suggest an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (MB-FLEACH), considering that the base station is Mobile. The innovation of our work is to intelligently select the cluster heads based on the mobility of the base station and its appearance probability in different locations. Thus, the selection of Cluster Head properly as supper cluster head node can reduce the energy consumption. In the proposed model, Mamdani’s fuzzy system is utilized with respect to the remaining energy, node centrality, mobility of base station and base station appearance probability as input variables. Our model was evaluated in two metrics of the lifetime for WSN in terms of Half Node Die and First Node Die and regarding energy consumption was conducted based on number of clusters and number of rounds. The simulation results using the OPNET simulator indicated in two scenarios that MB-FLEACH model was better than LEACH, LEACH-FL and CHEF models in the fields of lifetime and energy consumption for sensor nodes in the network.
Similar content being viewed by others
References
P. Maurya and A. Kaur, A survey on descendants of LEACH protocol, International Journal of Information Engineering and Electronic Business, Vol. 8, No. 2, pp. 46–58, 2016.
W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii International Conference on System Sciences, pages 1–10, 2000.
J. Tang, B. Hao and A. Sen, Relay node placement in large scale wireless sensor networks, Computer Communications, Vol. 29, No. 4, pp. 490–501, 2006.
M. Miao, J. Zander, K. W. Sung and S. B. Slimane, Fundamentals of Mobile Data Networks, Cambridge University PressCambridge, 2016. pp. 304–316.
W. B. Heinzelman, A. Chandrakasan and H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp. 660–670, 2002.
W. Guifeng, W. Yong, and T. Xiaoling, An ant colony clustering routing algorithm for wireless sensor networks. In Third International Conference on Genetic and Evolutionary Computing, pages 670–673, 2009.
O. Younis and S. Fahmy, HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, IEEE Transactions on Mobile Computing, Vol. 3, No. 4, pp. 366–379, 2004.
D. Zhixiang and Q. Bensheng, Three-layered routing protocol for WSN based on LEACH algorithm. In IET Conference on Wireless, Mobile and Sensor Networks, pages 72–75, 2007.
S. Lindsey, S. Raghavendra and K. M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics, IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No. 9, pp. 924–935, 2002.
J. Hong, J. Kook, S. Lee, D. Kwon and S. Yi, T-LEACH: The method of threshold-based cluster head replacement for wireless sensor networks, Information Systems Frontiers, Vol. 11, No. 5, pp. 513–521, 2009.
X. Min, S. Wei-Ren, J. Chang-Jiang and Z. Ying, Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks, AEU-International Journal of Electronics and Communications, Vol. 64, No. 4, pp. 289–298, 2010.
S. H. Kang and T. Nguyen, Distance based thresholds for cluster head selection in wireless sensor networks, IEEE Communications Letters, Vol. 16, No. 9, pp. 1396–1399, 2012.
A. Shahraki, M. K. Rafsanjani and A. B. Saeid, Hierarchical distributed management clustering protocol for wireless sensor networks, Telecommunication Systems, Vol. 65, No. 1, pp. 193–214, 2017.
G. Ran, H. Zhang and S. Gong, Improving on LEACH protocol of wireless sensor networks using fuzzy logic, Journal of Information and Computational Science, Vol. 7, No. 3, pp. 767–775, 2010.
P. Kumari, M. P. Singh, and P. Kumar, Survey of clustering algorithms using fuzzy logic in wireless sensor network. In International Conference on Energy Efficient Technologies for Sustainability (ICEETS), pages 924–928, 2013.
J. M. Kim, S. H. Park, Y. J. Han, and T. M. Chung, CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. In 10th International Conference on Advanced Communication Technology (ICACT), pages 654–659, 2008.
B. Baranidharan and B. Santhi, DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach, Applied Soft Computing, Vol. 40, pp. 495–506, 2016.
A. Alaybeyoglu, A distributed fuzzy logic-based root selection algorithm for wireless sensor networks, Computers and Electrical Engineering, Vol. 41, pp. 216–225, 2015.
J. Qin, W. Fu, H. Gao and W. X. Zheng, Distributed k-means algorithm and fuzzy c-means algorithm for sensor networks based on multiagent consensus theory, IEEE Transactions on Cybernetics, Vol. 47, No. 3, pp. 772–783, 2017.
A. Kaur and A. S. Buttar, Technologies, Energy efficient fuzzy logic based clustering algorithms in wireless sensor network: a survey, Journal of Network Communications and Emerging Technologies (JNCET), Vol. 5, No. 3, pp. 12–16, 2015.
W. Li, M. Bandai, and T. Watanabe, Tradeoffs among delay, energy and accuracy of partial data aggregation in wireless sensor networks. In 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pages 917–924, 2010.
A. A. Abbasi and M. Younis, A survey on clustering algorithms for wireless sensor networks, Computer Communications, Vol. 30, pp. 2826–2841, 2007.
Q. Bian, Y. Zhang, and Y. Zhao, Research on clustering routing algorithms in wireless sensor networks. In International Conference on Intelligent Computation Technology and Automation (ICICTA), pages 110–1113, 2010.
B. Balakrishnan and S. Balachandran, FLECH: fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. In Wireless Communications and Mobile Computing, pages 1–13, 2017.
L. A. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, Vol. 90, No. 2, pp. 111–127, 1997.
E. H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1–13, 1975.
P. Nayak and A. Devulapalli, A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime, IEEE Sensors Journal, Vol. 16, No. 1, pp. 137–144, 2016.
R. V. Kulkarni, A. Forster and G. K. Venayagamoorthy, Computational intelligence in wireless sensor networks: A survey, IEEE Communications Surveys and Tutorials, Vol. 13, No. 1, pp. 68–96, 2011.
A. Nejad, M. Arbabi and M. Romouzi, A survey on fuzzy based Clustering Routing Protocols in Wireless Sensor Networks: a new viewpoint, International Journal of Mechatronics Electrical and Computer Technology (IJEC), Vol. 4, No. 10, pp. 1186–1199, 2014.
I. Gupta, D. Riordan, and S. Sampalli, Cluster-head election using fuzzy logic for wireless sensor networks. In 3rd Annual Communication Networks and Services Research Conference (CNSR’05), pages 255–260, 2005.
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
Hosseini, S.M., Joloudari, J.H. & Saadatfar, H. MB-FLEACH: A New Algorithm for Super Cluster Head Selection for Wireless Sensor Networks. Int J Wireless Inf Networks 26, 113–130 (2019). https://doi.org/10.1007/s10776-019-00427-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10776-019-00427-w