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An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs

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Abstract

Due to advancement in the technology and need for machine-to-machine connectivity, wireless sensor network (WSN) overplays the role compared to other wireless networks. In this context, different applications based on WSNs need to be executed efficiently in terms of energy and communication. To achieve this, there is a need to collaborate among various devices at various levels. This can be achieved by the grouping of these devices, that is, through the clustering. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering-based hierarchical approach, efficient CH selection algorithm and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy-enhanced flower pollination algorithm-based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.

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References

  1. Afsar MM, Tayarani-N M (2014) Clustering in sensor networks: a literature survey. J Netw Comput Appl 46:198–226

    Google Scholar 

  2. Anisi MH, Abdul-Salaam G, Idris MYI, Wahab AWA, Ahmedy I (2015) Energy harvesting and battery power based routing in wireless sensor networks. Wirel Netw 23:249–266

    Google Scholar 

  3. Pantazis NA, Nikolidakis SA, Vergados DD (2013) Energy-Efficient routing protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 15(2):551–591

    Google Scholar 

  4. Halawani S, Khan AW (2010) Sensors lifetime enhancement techniques in wireless sensor networks—a survey. J Comput 2(5):34–47

    Google Scholar 

  5. Idris MYI, Znaid AMA, Wahab AWA, Qabajeh LK, Mahdi OA (2016) Low communication cost (LCC) scheme for localizing mobile wireless sensor networks. Wirel Netw 23:737–747

    Google Scholar 

  6. Heinzelman WB, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of 33rd annual Hawaii international conference on system sciences (HICSS-33), IEEE, 2000, pp 223. https://doi.org/10.1109/hicss.2000.926982

  7. 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

    Google Scholar 

  8. Manjeshwar A, Agrawal DP (2001) TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In: 15th international parallel and distributed processing symposium (IPDPS’01) workshops, USA, California, pp 2009–2015

  9. Attea BA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput 12:1950–1957. https://doi.org/10.1016/j.asoc.2011.04.007

    Google Scholar 

  10. Khalil EA, Attea BA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evolut Comput. https://doi.org/10.1016/j.swevo.2011.06.004

    Google Scholar 

  11. Khalil EA, Attea BA (2013) Stable-aware evolutionary routing protocol for wireless sensor networks. Wirel Pers Commun 69(4):1799–1817

    Google Scholar 

  12. Hussain S, Matin AW, Islam O (2007) Genetic algorithm for hierarchical wireless sensor networks. J Netw 2:87–97

    Google Scholar 

  13. Mittal N, Singh U, Sohi BS (2017) A novel energy efficient stable clustering approach for wireless sensor networks. Wirel Pers Commun 95:1–13

    Google Scholar 

  14. Kuila P, Jana PK (2014) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425

    Google Scholar 

  15. Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18:847–860

    Google Scholar 

  16. Mittal N, Singh U, Sohi BS (2017) Harmony search algorithm based threshold-sensitive energy-efficient clustering protocols for WSNs. Ad Hoc Sens Wirel Netw 36(1–4):149–174

    Google Scholar 

  17. Hoang DC, Yadav P, Kumar R, Panda SK (2014) Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Trans Industr Inf 10(1):774–783

    Google Scholar 

  18. Mittal N, Singh U, Sohi BS (2018) A boolean spider monkey optimization based energy efficient clustering approach for WSNs. Wirel Netw 24(6):2093–2109

    Google Scholar 

  19. Bennani K, Ghanami El D (2012) Particle swarm optimization based clustering in wireless sensor networks: the effectiveness of distance altering. In: International conference on complex systems (ICCS), Omaha, Nebraska, pp 1–4

  20. Yang XS (2012) Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp 240–249). Springer, Berlin Heidelberg

    Google Scholar 

  21. Singh U, Salgotra R (2016) Synthesis of linear antenna array using flower pollination algorithm. Neural Comput Appl 29:1–11

    Google Scholar 

  22. Draa A (2015) On the performances of the flower pollination algorithm—Qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Google Scholar 

  23. Singh U, Salgotra R (2017) Pattern synthesis of linear antenna arrays using enhanced flower pollination algorithm. Int J Antennas Propag, pp 1–11

    Google Scholar 

  24. Smaragdakis G, Matta I, Bestavros A (2004) SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In: Proceedings of international workshop on SANPA. http://open.bu.edu/xmlui/bitstream/handle/2144/1548/2004-022-sep.pdf?sequence=1

  25. Aderohunmu FA, Deng JD, Purvis MK (2011) Enhancing clustering in wireless sensor networks with energy heterogeneity. Int J Bus Data Commun Netw 7(4):18–32

    Google Scholar 

  26. Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor network. Comput Commun 29:2230–2237. https://doi.org/10.1016/j.comcom.2006.02.017

    Google Scholar 

  27. Kang SH, Nguyen T (2012) Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Commun Lett 16(9):1396–1399. https://doi.org/10.1109/LCOMM.2012.073112.120450

    Google Scholar 

  28. Kumar D, Aseri TC, Patel RB (2009) EEHC: energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput Commun 32:662–667. https://doi.org/10.1016/j.comcom.2008.11.025

    Google Scholar 

  29. Kumar D (2014) Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wirel Sens Syst 4(1):9–16. https://doi.org/10.1049/iet-wss.2012.0150

    Google Scholar 

  30. Tarhani M, Kavian YS, Siavoshi S (2014) SEECH: scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sens J 14(11):3944–3954. https://doi.org/10.1109/JSEN.2014.2358567

    Google Scholar 

  31. Aderohunmu FA, Deng JD, Purvis MK (2011) A deterministic energy-efficient clustering protocol for wireless sensor networks. In: Proceedings of 7th international conference on intelligent sensors, sensor networks and information processing (ISSNIP ‘11), IEEE, pp 341–346. https://doi.org/10.1109/issnip.2011.6146592

  32. Mittal N, Singh U (2015) Distance-based residual energy-efficient stable election protocol for WSNs. Arab J Sci Eng 40(6):1637–1646

    Google Scholar 

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

    Google Scholar 

  34. Manjeshwar A, Agrawal DP (2002) APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In: International parallel and distributed processing symposium, Florida, pp 195–202

  35. Adnan MdA, Razzaque MA, Ahmed I, Isnin IF (2014) Bio-Mimic optimization strategies in wireless sensor networks: a survey. Sensors 14:299–345. https://doi.org/10.3390/s140100299

    Google Scholar 

  36. Hussain S, Matin AW (2006) Hierarchical cluster-based routing in wireless sensor networks. In: IEEE/ACM International conference on information processing in sensor networks, IPSN, 2006

  37. Mittal N, Singh U, Sohi BS (2018) An energy aware cluster-based stable protocol for wireless sensor networks. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3542-x

    Google Scholar 

  38. Gupta I, Riordan D, Sampalli S (2005) Cluster-Head election using fuzzy logic for wireless sensor networks. In: 3rd annual communication networks and services research conference, pp 255–260

  39. Ran G, Zhang H, Gong S (2010) Improving on LEACH protocol of wireless sensor networks using Fuzzy Logic. J Inf Comput Sci 7:767–775

    Google Scholar 

  40. Kim JM, Park SH, Han YJ, Chung TM (2008) CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In: 10th international conference on advanced communication technology, vol 1, pp 654–659

  41. Sert SA, Bagci H, Yazici A (2015) MOFCA: multi-objective fuzzy clustering algorithm for wireless sensor networks. Appl Soft Comput 30:151–165

    Google Scholar 

  42. Tomar GS, Sharma T, Kumar B (2015) Fuzzy based ant colony optimization approach for wireless sensor network. Wirel Pers Commun 84:361–375

    Google Scholar 

  43. Tamandani YK, Bokhari MU (2015) SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network. Wirel Netw 22(2):647–653

    Google Scholar 

  44. Obaidy M Al, Ayesh A (2015) Energy efficient algorithm for swarmed sensors networks. Sustain Comput Inf Syst 5:54–63

    Google Scholar 

  45. Liu F, Lu J, Zhang G (2018) Unsupervised heterogeneous domain adaptation via shared fuzzy equivalence relations. IEEE Trans Fuzzy Syst 26(6):3555–3568

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, 140413, India

    Nitin Mittal

  2. Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, 147004, India

    Urvinder Singh, Rohit Salgotra & Manu Bansal

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  1. Nitin Mittal
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  2. Urvinder Singh
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  3. Rohit Salgotra
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  4. Manu Bansal
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Correspondence to Nitin Mittal.

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Mittal, N., Singh, U., Salgotra, R. et al. An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs. Neural Comput & Applic 32, 7399–7419 (2020). https://doi.org/10.1007/s00521-019-04251-4

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