Skip to main content

Advertisement

Springer Nature Link
Log in

Linear optimization and fuzzy-based clustering for WSNs assisted internet of things

  • 1215: Multimodal Interaction and IoT Applications
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Wireless data transmission on the Internet of Things (IoT) needs data-aware communication protocols. Clustering is one of the effective network management approaches that enhance the lifetime of IoT. The primary challenge in the data transmission across IoT is designing an energy-efficient clustering mechanism. Existing protocols struggle with the non-optimal selection of CHs and frequent re-clustering on IoT, which leads to significant energy consumption. If the cluster head (CH) lifetime of the devices (nodes) is known prior, then re-clustering can be avoided to a reasonable extent. Therefore, in this paper, we estimate the lifetime of devices as CHs by solving a linear optimization problem to extend the first node death as much as possible and also, stalls the frequent re-clustering process to minimize the energy consumption. We also apply the uniform distribution of CHs to ensure balanced energy consumption on IoT devices. The proposed clustering technique named ECFEL (Efficient Clustering using Fuzzy logic based on Estimated Lifetime) for IoT outperforms the existing protocols, namely Low Energy Adaptive Clustering Hierarchy (LEACH), MODified LEACH (MOD-LEACH), Dynamic k-LEACH (DkLEACH), Novel-PSO-LEACH, FM-SCHEL, and M-IWOCA techniques in terms of first node death (FND), half node death (HND), last node death (LND). Our simulation results showcase that ECFEL is having a better lifetime in terms of FND, HND, and LND, respectively. Furthermore, the experiments also confirm that ECFEL consumes less energy while maintaining a packet delivery ratio for a more extended period.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Ahmad A, Ahmed S, Imran M, Alam M, Niaz IA, Javaid N (2017) On energy efficiency in underwater wireless sensor networks with cooperative routing. Ann Telecommun 72(3-4):173–188

    Article  Google Scholar 

  2. Asghari P, Rahmani AM, Javadi HHS (2019) Internet of things applications: a systematic review. Comput Netw 148:241–261

    Article  Google Scholar 

  3. Bagci H, Yazici A (2010) An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In: International conference on fuzzy systems, IEEE, pp 1–8

  4. Dhumane AV, Prasad RS (2019) Multi-objective fractional gravitational search algorithm for energy efficient routing in iot. Wireless Netw 25(1):399–413

    Article  Google Scholar 

  5. Din S, Ahmad A, Paul A, Rathore MMU, Jeon G (2017) A cluster-based data fusion technique to analyze big data in wireless multi-sensor system. IEEE Access 5:5069–5083

    Article  Google Scholar 

  6. Ding X-X, Ling M, Wang Z-J, Song F-L (2017) Dk-leach: an optimized cluster structure routing method based on leach in wireless sensor networks. Wirel Pers Commun 96(4):6369–6379

    Article  Google Scholar 

  7. Falcon R, Jeon G, Bello R, Jeong J (2007) Learning collaboration links in a collaborative fuzzy clustering environment. In: Mexican international conference on artificial intelligence, Springer, pp 483– 495

  8. Farahani M, Rahbar AG (2019) Double leveled unequal clustering with considering energy efficiency and load balancing in dense iot networks. Wirel Pers Commun 106(3):1183–1207

    Article  Google Scholar 

  9. Haseeb K, Lee S, Jeon G (2020) Ebds: An energy-efficient big data-based secure framework using internet of things for green environment. Environmental Technology & Innovation, pp 101129

  10. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, IEEE, pp 10–pp

  11. Huang J, Ruan D, Meng W (2018) An annulus sector grid aided energy-efficient multi-hop routing protocol for wireless sensor networks. Comput Netw 147:38–48

    Article  Google Scholar 

  12. Hussain F, Hussain R, Hassan SA, Hossain E (2020) Machine learning in iot security: current solutions and future challenges. IEEE Communications Surveys & Tutorials

  13. Jianyin L (2012) Simulation of improved routing protocols leach of wireless sensor network. In: 2012 7Th international conference on computer science & education (ICCSE), IEEE, pp 662–666

  14. Lee J-S, Cheng W-L (2012) Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors J 12(9):2891–2897

    Article  Google Scholar 

  15. Li J, Silva BN, Diyan M, Cao Z, Han K (2018) A clustering based routing algorithm in iot aware wireless mesh networks. Sustainable Cities and Society 40:657–666

    Article  Google Scholar 

  16. Lin J, Duan G, Tian Z (2020) Interval intuitionistic fuzzy clustering algorithm based on symmetric information entropy. Symmetry 12(1):79

    Article  Google Scholar 

  17. Lin D, Wang Q, Min W, Jianfeng X u, Zhang Z (2020) A survey on energy-efficient strategies in static wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) 17(1):1–48

    Article  Google Scholar 

  18. Liu X, Fu L, Wang J, Wang X, Chen G (2019) Multicast scaling of capacity and energy efficiency in heterogeneous wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) 15(3):1–32

    Google Scholar 

  19. Logambigai R, Kannan A (2016) Fuzzy logic based unequal clustering for wireless sensor networks. Wirel Netw 22(3):945–957

    Article  Google Scholar 

  20. Mahmood D, Javaid N, Mahmood S, Qureshi S, Memon AM, Zaman T (2013) Modleach: a variant of leach for wsns. In: 2013 Eighth international conference on broadband and wireless computing, communication and applications, IEEE, pp 158–163

  21. Majadi N, Mahmud SR, Sultana M (2013) Uniform distribution technique of cluster heads in leach protocol. International Journal on Recent Trends in Engineering & Technology 8(1):84

    Google Scholar 

  22. Maratha P et al (2019) A comparative study on prominent strategies of cluster head selection in wireless sensor networks. In: Integrated intelligent computing, communication and security, Springer, pp 373–384

  23. Mirzaie M, Mazinani SM (2017) Adaptive mcfl: an adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network. Comput Commun 111:56–67

    Article  Google Scholar 

  24. Orlin J (2013) LP Transformation techniques. Spring

  25. Pourghebleh B, Hayyolalam V (2019) A comprehensive and systematic review of the load balancing mechanisms in the internet of things. Clust Comput, pp 1–21

  26. Qureshi KN, Tayyab MQ, Rehman SU, Jeon G (2020) An interference aware energy efficient data transmission approach for smart cities healthcare systems. Sustainable Cities and Society 62:102392

    Article  Google Scholar 

  27. Rostami AS, Badkoobe M, Mohanna F, Hosseinabadi AAR, Sangaiah AK et al (2018) Survey on clustering in heterogeneous and homogeneous wireless sensor networks. J Supercomput 74(1):277–323

    Article  Google Scholar 

  28. Sathya Lakshmi Preeth SK, Dhanalakshmi R, Kumar R, Mohamed Shakeel P (2018) An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for wsn-assisted iot system. Journal of Ambient Intelligence and Humanized Computing, pp 1–13

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

    Article  Google Scholar 

  30. Shahraki A, Taherkordi A, Haugen Ø, Eliassen F (2020) Clustering objectives in wireless sensor networks: a survey and research direction analysis. Comput Netw 180:107376

    Article  Google Scholar 

  31. Sharma R, Vashisht V, Singh U (2019) Fuzzy modelling based energy aware clustering in wireless sensor networks using modified invasive weed optimization. Journal of King Saud University-Computer and Information Sciences

  32. Singh AK, Purohit N, Varma S (2013) Fuzzy logic based clustering in wireless sensor networks: a survey. Int J Electron 100(1):126–141

    Article  Google Scholar 

  33. Souri A, Hussien A, Hoseyninezhad M, Norouzi M (2019) A systematic review of iot communication strategies for an efficient smart environment. Transactions on Emerging Telecommunications Technologies, pp e3736

  34. Sreenivasamurthy S, Obraczka K (2018) Clustering for load balancing and energy efficiency in iot applications. In: 2018 IEEE 26Th international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS), IEEE, pp 319–332

  35. Sung Y, Lee S, Lee M (2018) A multi-hop clustering mechanism for scalable iot networks. Sensors 18(4):961

    Article  Google Scholar 

  36. Thiagarajan R et al (2020) Energy consumption and network connectivity based on novel-leach-pos protocol networks. Comput Commun 149:90–98

    Article  Google Scholar 

  37. Ullah MF, Imtiaz J, Maqbool KQ (2019) Enhanced three layer hybrid clustering mechanism for energy efficient routing in iot. Sensors 19(4):829

    Article  Google Scholar 

  38. Uma Maheswari D, Sudha S (2019) Node degree based energy efficient two-level clustering for wireless sensor networks. Wirel Pers Commun 104 (3):1209–1225

    Article  Google Scholar 

  39. Wazid M, Das AK, Hussain R, Succi G, Rodrigues JJPC (2019) Authentication in cloud-driven iot-based big data environment: survey and outlook. J Syst Archit 97:185–196

    Article  Google Scholar 

  40. Xu X, Ansari R, Khokhar A, Vasilakos AV (2015) Hierarchical data aggregation using compressive sensing (hdacs) in wsns. ACM Transactions on Sensor Networks (TOSN) 11(3):1–25

    Article  Google Scholar 

  41. Xu L, Collier R, O’Hare GMP (2017) A survey of clustering techniques in wsns and consideration of the challenges of applying such to 5g iot scenarios. IEEE Internet Things J. 4(5):1229–1249

    Article  Google Scholar 

  42. Xu Y, Yue Z, Lv L (2019) Clustering routing algorithm and simulation of internet of things perception layer based on energy balance. IEEE Access 7:145667–145676

    Article  Google Scholar 

Download references

Acknowledgments

We thank Ashish Kumar Luhach from The PNG University of Technology, Papua New Guinea and Arnab Chakroborty from the Indian Statistical Institute, Kolkata for their constructive ideas and insightful discussion on improving the outlook of the paper. Priti Maratha, as part of the National Eligibility Test-Junior Research Fellowship scheme with Reference ID- 3361/(NET-JUNE 2015), acknowledges the assistance from University Grant Commission, New Delhi.

Author information

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology Kurukshetra, Haryana, India

    Priti Maratha & Kapil Gupta

Authors
  1. Priti Maratha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kapil Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priti Maratha.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maratha, P., Gupta, K. Linear optimization and fuzzy-based clustering for WSNs assisted internet of things. Multimed Tools Appl 82, 5161–5185 (2023). https://doi.org/10.1007/s11042-021-11850-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11850-8

Keywords

Search

Navigation