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EEFMCP: energy efficient fuzzy logic-based multi-clustering protocol

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Abstract

Each round of clustering generally uses a single method to perform the task of clustering. For the enhancement of network capability to better manage the network resources, we extend a new multi-clustering algorithm (EEFMCP) in this paper. We apply a fuzzy inference system to define the various rules in each execution round. Three types of input/ output combinations define the complete working of the clustering protocol. For each node, the different execution round adopts a specific input/output combination to calculate the chance value for cluster head (CH). Multiple input/ output combination of fuzzy variables has better control over the network dynamism. The network feature drastically changes due to the depletion of the energy of sensor nodes. A new closeness index is proposed and utilized for better CH selection. We compare EEFMCP with Low Energy Adaptive Clustering Hierarchy (LEACH), Cluster Head Election mechanism using Fuzzy logic (CHEF), Fuzzy Energy-Aware Unequal Clustering Algorithm (EAUCF), and Fuzzy Logic Based Energy Efficient Clustering Hierarchy (FLECH), the distinct algorithms useful for clustering in WSN. The substantial simulation work shows that the EEFMCP always performs better for different simulation scenarios.

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References

  • Abdul-Qawy ASH, Srinivasulu T (2019) SEES: a scalable and energy-efficient scheme for green IoT-based heterogeneous wireless nodes. J Ambient Intell Humaniz Comput 10:1571–1596

    Article  Google Scholar 

  • Alaba FA, Othman M, Hashem IAT, Alotaibi F (2017) Internet of things security: a survey. J Netw Comput Appl 88:10–28

    Article  Google Scholar 

  • Avci B, Trajcevski G, Tamassia R, Scheuermann P, Zhou F (2017) Efficient detection of motion-trend predicates in wireless sensor networks. Comput Commun 101:26–43

    Article  Google Scholar 

  • Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13(4):1741–1749

    Article  Google Scholar 

  • Balakrishnan B, Balachandran S (2017) FLECH: fuzzy logic based energy efficient clustering hierarchy for non-uniform wireless sensor networks. Wirel Comm Mobile Comput. https://doi.org/10.1155/2017/1214720

    Article  Google Scholar 

  • Bokhari TYK, M U, (2016) SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network. Wireless Netw 22:647–653

    Article  Google Scholar 

  • Bozorgi SM, Bidgoli AM (2019) HEEC: a hybrid unequal energy efficient clustering for wireless sensor networks. Wireless Netw 25:4751–4772

    Article  Google Scholar 

  • Cui JH, Kong J, Gerla M, Zhou S (2006) The challenges of building mobile underwater wireless networks for aquatic applications. IEEE Netw 20(3):12–18

    Article  Google Scholar 

  • Fanian F, Rafsanjani MK (2018) Memetic fuzzy clustering protocol for wireless sensor networks: shuffled frog leaping algorithm. Appl Soft Comput 71:568–590

    Article  Google Scholar 

  • Fanian F, Rafsanjani MK (2020) A new fuzzy multi-hop clustering protocol with automatic rule tuning for wireless sensor networks. Appl Soft Comput 89:106–115

    Article  Google Scholar 

  • Gajjar SH, Pradhan SN, Dasgupta KS (2011) Wireless sensor network: application led research perspective. In Recent advances in intelligent computational systems (RAICS). (pp. 025–030). IEEE

  • Gajjar S, Choksi N, Sarkar M, Dasgupta K (2014) Comparative analysis of wireless sensor network motes. In Signal processing and integrated networks (SPIN) (pp. 426–431). IEEE

  • Haseeb K et al (2017) Adaptive energy aware cluster-based routing protocol for wireless sensor networks. Wireless Netw 23(6):1953–1966

    Article  Google Scholar 

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000 (pp. 1–10)

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1:660–670

    Article  Google Scholar 

  • Jafarizadeh V, Keshavarzi A, Derikvand T (2017) Efficient cluster head selection using Naive Bayes classifier for wireless sensor networks. Wireless Netw 23(3):779–785

    Article  Google Scholar 

  • Jain A, Goel AK (2020) Energy efficient fuzzy routing protocol for wireless sensor networks. Wireless Pers Commun 110:1459–1474

    Article  Google Scholar 

  • Jiang Y, Zhang L, Wang L (2013) Wireless sensor networks and the internet of things. Int J Distributed Sensor Netw

  • Jung K et al (2017) Improving adaptive cluster head selection of teen protocol using fuzzy logic for WMSN. Multimedia Tools Appl 2017:18175–18190

    Article  Google Scholar 

  • Khelladi L, Djenouri D, Rossi M, Badache N (2017) Efficient on-demand multi-node charging techniques for wireless sensor networks. Comput Commun 101:44–56

    Article  Google Scholar 

  • Kim J, Park S, Han Y, Chung T (2008) CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. Adv Commun Technol 1:654–659

    Google Scholar 

  • Li J, Silva B, Diyan M, Cao Z, Han K (2018a) A clustering based routing algorithm in IoT aware Wireless Mesh Networks. Sustain Cities Soc 40:657–666

    Article  Google Scholar 

  • Li S, Da-Xu L, Zhao S (2018b) 5G internet of things: a survey. J Ind Inf Integr 10:1–9

    Google Scholar 

  • Lin D, Wang Q (2017) A game theory based energy efficient clustering routing protocol for WSNs. Wireless Netw 23(4):1101–1111

    Article  Google Scholar 

  • Mittal N, Singh U, Sohi BS (2017) A stable energy efficient clustering protocol for wireless sensor networks. Wireless Netw 23(6):1809–1821

    Article  Google Scholar 

  • Negnevitsky M (2001) Artificial intelligence: a guide to intelligent systems, Addison- Wesley, Reading

  • Ni Q, Pan Q, Du H, Cao C, Zhai Y (2017) A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization. IEEE/ACM Trans Comput Biol Bioinf 14 (1)

  • Patil HK, Chen TM (2017) Wireless sensor network security: the internet of things, in: Computer and Information Security Handbook, Third Ed., Elsevier, pp. 317–337

  • Rashid B, Rehmani MH (2016) Applications of wireless sensor networks for urban areas: a survey. J Netw Comput Appl 60:192–219

    Article  Google Scholar 

  • Rault T, Bouabdallah A, Challal Y (2007) Energy efficiency in wireless sensor networks: a top-down survey. J Comput Netw 67:104–122

    Article  Google Scholar 

  • Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput 30:1–10

    Article  Google Scholar 

  • Sharma S, Sethi D, Bhattacharya P (2015) Artificial neural network based cluster head selection in wireless sensor network. Int J Comput Appl 119(4):34–41

    Google Scholar 

  • Su S, Zhao S (2018) An optimal clustering mechanism based on Fuzzy-C means for wireless sensor networks. Sustain Comput Inf Syst 18:127–134

    Google Scholar 

  • Vijayalakshmi K, Anandan PA (2018) A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Clust Comput 22(6):12275–12282

    Google Scholar 

  • Wang F, Liu J (2011) Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Commun Surv Tutorials 13(4):673–687

    Article  Google Scholar 

  • Wang D, Lin L, Xu L (2011) A study of subdividing hexagon-clustered WSN for power saving: analysis and simulation. Ad Hoc Netw 9(7):1302–1311

    Article  Google Scholar 

  • Yager RR, Espada JP (2018) New advances in the internet of things. Springer

    Book  Google Scholar 

  • Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. J Comput Netw 52(12):2292–2330

    Article  Google Scholar 

  • Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. G.B. Pant University of Agriculture and Technology, Pantnagar, Uttrakhand, India

    Pankaj Kumar Mishra

  2. Govind Ballabh Pant Institute of Engineering and Technology, Ghurdauri, Pauri Garhwal, Uttrakhand, India

    Shashi Kant Verma

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  1. Pankaj Kumar Mishra
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  2. Shashi Kant Verma
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Correspondence to Pankaj Kumar Mishra.

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Mishra, P.K., Verma, S.K. EEFMCP: energy efficient fuzzy logic-based multi-clustering protocol. J Ambient Intell Human Comput 14, 1991–2005 (2023). https://doi.org/10.1007/s12652-021-03412-5

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  • DOI: https://doi.org/10.1007/s12652-021-03412-5

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