Skip to main content

Advertisement

Log in

A new clustering approach in wireless sensor networks using fuzzy system

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In recent years, wireless sensor networks (WSNs) have attracted many researchers due to their widely usage in a wide range of applications. One of the most important problems in these networks is energy consumption that has a direct effect on network lifetime. Clustering is one of the most important solutions in order to overcome the problem. Energy resource limitation is a fundamental problem in WSNs and clustering protocols provide suitable procedures in order to enhance network lifetime. However, they impose high energy consumption on cluster heads (CH), and therefore, in each round, the protocol should reform clusters and change CH in order to enhance network lifetime. Although these protocols are proper for clustering, do not guarantee suitable CH selection. In this paper, a novel energy-efficient method is proposed using fuzzy logic and three parameters including the amount of energy in CH, distance from CH to base station, and the number of connections in CH. In fact, we focus on the cluster formation process. The proposed model is compared to the well-known low-energy adaptive clustering hierarchy protocol. Simulation results demonstrate that the proposed protocol improves network lifetime.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Rostami AS, Bernety HM, Hosseinabadi AR (2011) A novel and optimized algorithm to select monitoring sensors by GSA. In: International Conference on Control, Instrumentation and Automation ICCIA), pp 829–834

  2. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114

    Article  Google Scholar 

  3. Estrin D, Culler D, Pister K, Sukhatme G (2002) Connecting the physical world with pervasive networks. IEEE Pervasive Comput 1:59–69

    Article  Google Scholar 

  4. Karl H, Willig A (2005) Protocols and architectures for wireless sensor networks. British Library, ISBN-13 978-0-470-09510-2 (HB), 1-507

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

    Google Scholar 

  6. Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput 26:1182–1191

    Article  MATH  Google Scholar 

  7. Tabatabaei S, Teshnehlab M, Mirabedini SJ (2015) Fuzzy-based routing protocol to increase throughput in mobile ad hoc networks. Wirel Pers Commun 84(4):2307–2325

    Article  Google Scholar 

  8. Golden Julie E, Tamil Selvi S (2016) Development of energy efficient clustering protocol in wireless sensor network using neuro-fuzzy approach. Sci World J 2016:1–8

    Article  Google Scholar 

  9. Esmaeeli M, Hosseini Ghahroudi SA (2015) An energy-efficiency protocol in wireless sensor networks using theory of games and fuzzy logic. Int J Comput Appl 126(1):8–13

    Google Scholar 

  10. Abood B, Hussien A, Li Y, Wang D (2016) Energy efficient clustering in wireless sensor networks using fuzzy approach to improve LEACH protocol. Int J Manag Inf Technol 11(2):2641–2656

    Google Scholar 

  11. Wankhade NR, Choudhari DN (2015) Energy efficient unequal clustering algorithm for clustered wireless sensor network. Int J Tech Res Appl 3(3):195–198

    Google Scholar 

  12. Mhemed R, Aslam N, Phillips W, Comeau F (2012) An energy efficient fuzzy logic cluster formation protocol in wireless sensor networks. In: The 3rd International Conference on Ambient Systems, Networks and Technologies (ANT), vol 10, pp 255–262

  13. Cheng-Kui Huang T, Hsu W, Chen Y (2013) Conjecturable knowledge discovery: a fuzzy clustering approach. Fuzzy Sets Syst 221:1–23

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  15. Geetha V, Kallapur PV, Tellajeera S (2012) Clustering in wireless sensor networks: performance comparison of LEACH & LEACH-C protocols using NS2. Proced Technol 4:163–170

    Article  Google Scholar 

  16. Siew ZW, Kiring A, Yew HT, Neelakantan P, Teo KTK (2011) Energy efficient clustering algorithm in wireless sensor networks using fuzzy logic control. In: IEEE Colloquium on Humanities, Science and Engineering (CHUSER), pp 392–397

  17. Aslam N, Phillips W, Robertson W, Sivakumar S (2011) A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks. Inf Fusion 12(3):202–212

    Article  Google Scholar 

  18. Khachane D, Shrivastav A (2016) Wireless sensor network and its applications in automobile industry. Int Res J Eng Technol (IRJET) 3:2214–2220

    Google Scholar 

  19. Jo Y, Choi J, Jung I (2014) Traffic information acquisition system with ultrasonic sensors in wireless sensor networks. Int J Distrib Sens Netw 2014:1–12

    Google Scholar 

  20. Zahedi ZM, Akbari R, Shokouhifar M, Safaei F, Jalali A (2016) Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Syst Appl 55:313–328

    Article  Google Scholar 

  21. Gui T, Ma C, Wang F, W DE (2016) Survey on swarm intelligence based routing protocols for wireless sensor networks. In: IEEE International Conference on Industrial Technology (ICIT), pp 1944–1949

  22. Wang Y, Chen Y (2014) A comparison of Mamdani and Sugeno fuzzy inference systems for traffic flow prediction. J Comput 9:12–21

    Google Scholar 

  23. Yel E, Yalpir S (2011) Prediction of primary treatment effluent parameters by fuzzy inference system (FIS) approach. Proced Comput Sci 3:659–665

    Article  Google Scholar 

  24. Singh SK, Kumar P, Singh JP (2017) A survey on successors of LEACH protocol. IEEE Transl 5:4298–4328

    Google Scholar 

  25. Pantazis NA, Nikolidakis SA, Vergados DD, Member S (2013) Energy-efficient routing protocols in wireless sensor networks. IEEE Commun Surv Tutor 15:551–591

    Article  Google Scholar 

  26. Intanagonwiwat Ch, Govindan R, Estrin D, Heidemann J, Silva F (2003) Directed diffusion for wireless sensor networking. IEEE/ACM Trans Netw 11:1–15

    Article  Google Scholar 

  27. 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 

  28. YE M, LI Ch, CHEN G, WU J (2006) An energy efficient clustering scheme in wireless sensor networks. Ad Hoc Sens Wirel Netw 3:99–199

    Google Scholar 

  29. Hong J, Kook J, Lee S, Kwon D, Yi S (2009) T-LEACH: the method of threshold-based cluster head replacement for wireless sensor networks. Inf Syst Front 11(5):513–521

    Article  Google Scholar 

  30. Aslam M, Javaid N, Rahim A, Nazir U, Bibi A, Khan ZA (2012) Survey of extended LEACH-based clustering routing protocols for wireless sensor networks. In: 5th International Symposium on Advances of High Performance Computing and Networking (AHPCN-2012) in Connection with 14th IEEE International Conference on High Performance Computing and Communications (HPCC-2012), pp 25–27

  31. Anam S, Yadav OP (2017) Performance enhancement of leach protocol in wireless sensor network in terms of network life time. Int J Technol Res Eng 4:1060–1063

    Google Scholar 

  32. Handy MJ, Haase M, Timmermann D (2002) Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In: Fourth IEEE Conference on Mobile and Wireless Communications Networks, pp 368–372

  33. Voigt Th, Dunkels A, Alonso J, Ritter H, Schiller J (2004) Solar-aware clustering in wireless sensor networks. In: Ninth International Symposium on Computers and Communications, pp 1–6

  34. Kumar V, Janjeey S, Tiwari S, Member I (2011) Energy efficient clustering algorithms in wireless sensor networks. IJCSI Int J Comput Sci Issues 8(5):259–268

    Google Scholar 

  35. Mittal N, Singh DP, Panghal A, Chauhan RS (2010) Improved leach communication protocol for WSN. In: National Conference on Computational Instrumentation, pp 151–155

  36. Long Liu J, Ravishankar Ch.V (2011) Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. In: International Journal of Machine Learning and Computing, vol 1, no 1

  37. Abdulsalam HM, Ali BA (2013) W-LEACH based dynamic adaptive data aggregation algorithm for wireless sensor networks. Int J Distrib Sens Netw 1:1–11

    Google Scholar 

  38. Tripathi M, Battula RB, Gau MS, Laxmi V (2013) Energy efficient clustered routing for wireless sensor network. In: International Conference on Mobile Ad-hoc and Sensor Networks, pp 330–335

  39. Eletreby RM, Elsayed HM, Khairy MM (2014) A spectrum aware clustering protocol for cognitive radio sensor networks. In: International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), pp 179–184

  40. Tang Ch, Tan Q, Han Y, An W, Li H, Tang H (2016) An energy harvesting aware routing algorithm for hierarchical clustering wireless sensor networks. Ksii Trans Internet Inf Syst 10(2):504–521

    Google Scholar 

  41. Buratti CH, Giorgetti A, Verdone R (2005) Cross-layer design of an energy-efficient cluster formation algorithm with carrier-sensing multiple access for wireless sensor networks. EURASIP J Wirel Commun Netw 2005:672–685

    Article  MATH  Google Scholar 

  42. Loscrì V, Morabito G, Marano S (2005) A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In: 62nd Vehicular Technology Conference VTC, pp 1809–1813

  43. Xiangning F, Yulin S (2007) Improvement on LEACH protocol of wireless sensor network. In: International Conference on Sensor Technologies and Applications, pp 260–264

  44. Kumar GS, MV VP, Jacob KP (2008) Mobility metric based LEACH-mobile protocol. In: 16th International Conference on Advanced Computing and Communications, ADCOM 2008, pp 248–253

  45. Farooq MO, Dogar AB, Shah GhA (2010) Multi-hop routing with low energy adaptive clustering hierarchy. In: Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM), pp 262–268

  46. Yektaparast A, Nabav F-H, Sarmast A (2012) An improvement on LEACH protocol. In: 14th International Conference on Advanced Communication Technology (ICACT), pp 992–996

  47. Gopi Saminathan A, Karthik S (2013) DAO-LEACH: an approach for energy efficient routing based on data aggregation and optimal clustering in WSN. Life Sci J 10:380–389

    Google Scholar 

  48. Zhang H, Zhang Sh, Bu W (2014) A clustering routing protocol for energy balance of wireless sensor network based on simulated annealing and genetic algorithm. Int J Hybrid Inf Technol 7:71–82

    Article  Google Scholar 

  49. Cho S, Han L, Joo B, Han S (2014) An efficient cluster-based technique to track mobile sinks in wireless sensor networks. Int J Distrib Sens Netw 2014:1–10

    Google Scholar 

  50. Arumugam GS, Ponnuchamy TH (2015) EE-LEACH: development of energy-efficient LEACH protocol for data gathering in WSN. EURASIP J Wirel Commun Netw 76:1–9

    Google Scholar 

  51. Khoshkangini R, Zaboli S, Sampalli S (2013) Energy efficient clustering using fuzzy logic. Int J Comput Sci Mob Comput 2:8–14

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Homayun Motameni.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toloueiashtian, M., Motameni, H. A new clustering approach in wireless sensor networks using fuzzy system. J Supercomput 74, 717–737 (2018). https://doi.org/10.1007/s11227-017-2153-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-017-2153-0

Keywords

Navigation