Skip to main content

Advertisement

SpringerLink
Log in

Field-clustering with sleep awake mechanism with fuzzy in wireless sensor network

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Wireless Sensor Networks (WSN) have widely grown worldwide and are utilized in all innovative applications, yet there are problems such as data overloading, packet drop, and lower data transmission rate. All these issues have been engendered due to the high energy consumption problem. The node consumes more energy and has less lifetime that might be disabled during the data transmission. To address these problems, a novel Wolf Fuzzy-based Aggregator Node selection (WFbANS) protocol is designed for the WSN environment. Initially, the node's parameters were validated and selected for the cluster hub. Consequently, the workless node has been identified to enable the sleep state. Furthermore, the planned energy-optimized model is tested in the MATLAB environment. Finally, the data was transferred, and the communication parameters were noted and compared with other models. The presented model has recorded the finest throughput rate of 350 Kbps, a data transfer rate of 99.7%, less energy consumption of 0.7 J, less packet drop of 2%, and a minor communication delay of 90 ms.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Priyadarshi R, Rawat P, Nath V, Acharya B (2020) Three level heterogeneous clustering protocol for wireless sensor network. Microsyst Technol 26:3855–3864. https://doi.org/10.1007/s00542-020-04874-x

    Article  Google Scholar 

  2. Alghamdi TA (2020) Energy efficient protocol in wireless sensor network: optimized cluster head selection model. Telecommun Syst 74:331–345. https://doi.org/10.1007/s11235-020-00659-9

    Article  Google Scholar 

  3. Sahoo BM, Amgoth T, Pandey HM (2020) Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Netw 106:102237. https://doi.org/10.1016/j.adhoc.2020.102237

    Article  Google Scholar 

  4. Kumar B, Tiwari UK, Kumar S (2020) Energy efficient quad clustering based on K-means algorithm for wireless sensor network. Int Conf Parallel Distrib Grid Comput (PDGC), IEEE. https://doi.org/10.1109/PDGC50313.2020.9315853

    Article  Google Scholar 

  5. Piltyay S, Bulashenko A, Demchenko I (2020) Wireless sensor network connectivity in heterogeneous 5G mobile systems. IEEE Int Conf Prob Infocommun Sci Technol (PIC S&T), IEEE. https://doi.org/10.1109/PICST51311.2020.9468073

    Article  Google Scholar 

  6. Mishra PK, Verma SK (2020) A survey on clustering in wireless sensor network. Int Conf Comput Commun Netw Technol (ICCCNT), IEEE. https://doi.org/10.1109/ICCCNT49239.2020.9225420

    Article  Google Scholar 

  7. Osamy W, El-Sawy AA, Salim A (2020) CSOCA: Chicken swarm optimization based clustering algorithm for wireless sensor networks. IEEE Access 8:60676–60688. https://doi.org/10.1109/ACCESS.2020.2983483

    Article  Google Scholar 

  8. Babaeer HA, Al-Ahmadi SA (2020) Efficient and secure data transmission and sinkhole detection in a multi-clustering wireless sensor network based on homomorphic encryption and watermarking. IEEE Access 8:92098–92109. https://doi.org/10.1109/ACCESS.2020.2994587

    Article  Google Scholar 

  9. Sibi SA, Prabhu RV (2020) Survey on clustering and depletion of energy in wireless sensor network. Int Conf Intell Sustain Syst (ICISS), IEEE. https://doi.org/10.1109/ICISS49785.2020.9316011

    Article  Google Scholar 

  10. Pang A, Chao F, Zhou H, Zhang J (2020) The method of data collection based on multiple mobile nodes for wireless sensor network. IEEE Access 8:14704–14713. https://doi.org/10.1109/ACCESS.2020.2966652

    Article  Google Scholar 

  11. Rajesh D, Jaya T (2020) A mathematical model for energy efficient secured CH clustering protocol for mobile wireless sensor network. Wirel Pers Commun 112:421–438. https://doi.org/10.1007/s11277-020-07036-4

    Article  Google Scholar 

  12. Mohapatra H, Rath AK, Landge PB, Bhise D, Panda S, Gayen S (2020) A comparative analysis of clustering protocols of wireless sensor network. Int J Mech Prod Eng Res Dev (IJMPERD) ISSN (P) 10(3):2249–6890

    Google Scholar 

  13. Tripathi Y, Prakash A, Tripathi R (2022) A sleep scheduling based cooperative data transmission for wireless sensor network. Int J Electron 109(4):596–616. https://doi.org/10.1080/00207217.2021.1914193

    Article  Google Scholar 

  14. Anand S, Manoj KC (2020) A survey on clustering approaches to strengthen the performance of wireless sensor network. Int Conf Inventive Res Comput Appl (ICIRCA), IEEE. https://doi.org/10.1109/ICIRCA48905.2020.9183174

    Article  Google Scholar 

  15. Gupta B, Rana S, Sharma A (2020) An efficient data aggregation approach for prolonging lifetime of wireless sensor network. Int Confe Innov Comput Commun 137–147. Springer, Singapore. https://doi.org/10.1007/978-981-15-0324-5_12

  16. Kulshrestha R, Ramani P (2021) Descriptive study and characterization of energy efficient clustering protocols for wireless sensor networks. J Inst Eng India Ser B 102(2):377–385. https://doi.org/10.1007/s40031-021-00535-3

    Article  Google Scholar 

  17. Bhandari RR, Rajasekhar K (2020) Energy-efficient routing-based clustering approaches and sleep scheduling algorithm for network lifetime maximization in sensor network: a survey. Inventive Commun Comput Technol 293–306. Springer, Singapore. https://doi.org/10.1007/978-981-15-0146-3_27

  18. Shagari NM, Idris MYI, Salleh RB, Ahmedy I, Murtaza G, Shehadeh HA (2020) Heterogeneous energy and traffic aware sleep-awake cluster-based routing protocol for wireless sensor network. IEEE Access 8:12232–12252. https://doi.org/10.1109/ACCESS.2020.2965206

    Article  Google Scholar 

  19. Safa’a SS, Mabrouk TF, Tarabishi RA (2021) An improved energy-efficient head election protocol for clustering techniques of wireless sensor network (June 2020). Egypt Inform J 22(4):439–445. https://doi.org/10.1016/j.eij.2021.01.003

    Article  Google Scholar 

  20. Dhunna GS, Al-Anbagi I (2019) A low power WSNs attack detection and isolation mechanism for critical smart grid applications. IEEE Sens J 19(13):5315–5324. https://doi.org/10.1109/JSEN.2019.2902357

    Article  Google Scholar 

  21. Radhika S, Rangarajan P (2021) Fuzzy based sleep scheduling algorithm with machine learning techniques to enhance energy efficiency in wireless sensor networks. Wirel Pers Commun 118:3025–3044. https://doi.org/10.1007/s11277-021-08167-y

    Article  Google Scholar 

  22. Liu W, Shoji Y, Shinkuma R (2017) Logical correlation-based sleep scheduling for WSNs in ambient-assisted homes. IEEE Sens J 17(10):3207–3218. https://doi.org/10.1109/JSEN.2017.2687441

    Article  Google Scholar 

  23. Moridi E, Haghparast M, Hosseinzadeh M, Jassbi SJ (2020) Novel fault-tolerant clustering-based multipath algorithm (FTCM) for wireless sensor networks. Telecommun Syst 74(4):411–424. https://doi.org/10.1007/s11235-020-00663-z

    Article  Google Scholar 

  24. Moridi E, Haghparast M, Hosseinzadeh M, Jassbi SJ (2022) A novel hierarchical fault management framework for wireless sensor networks: HFMF. Peer Peer Netw Appl 15(1):45–55. https://doi.org/10.1007/s12083-021-01226-y

    Article  Google Scholar 

  25. Sah DK, Amgoth T (2020) A novel efficient clustering protocol for energy harvesting in wireless sensor networks. Wirel Netw 26:4723–4737. https://doi.org/10.1007/s11276-020-02351-x

    Article  Google Scholar 

  26. Ahmed G, Zhao X, Fareed MMS, Asif MR, Raza SA (2019) Data redundancy-control energy-efficient multi-hop framework for wireless sensor networks. Wirel Pers Commun 108:2559–2583. https://doi.org/10.1007/s11277-019-06538-0

    Article  Google Scholar 

  27. Thekkil TM, Prabakaran N (2021) Optimization based multi-objective weighted clustering for remote monitoring system in WSN. Wirel Pers Commun 117:387–404. https://doi.org/10.1007/s11277-020-07874-2

    Article  Google Scholar 

  28. Sharma A, Kaushal M, Khehra BS (2017) Proposal and evaluation of a fuzzy logic-driven resource allocation mechanism. Int J Fuzzy Syst 19:383–399. https://doi.org/10.1007/s40815-016-0185-x

    Article  Google Scholar 

  29. Mohanty SN, Lydia EL, Elhoseny M, Al Otaibi MMG, Shankar K (2020) Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks. Phys Commun 40:101097. https://doi.org/10.1016/j.phycom.2020.101097

    Article  Google Scholar 

  30. Mehta D, Saxena S (2020) MCH-EOR: Multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks. Sustain Comput Inform Syst 28:100406. https://doi.org/10.1016/j.suscom.2020.100406

    Article  Google Scholar 

  31. Thiagarajan R, Babu MR, Moorthi M (2021) Quality of service based Ad hoc on-demand multipath distance vector routing protocol in mobile ad hoc network. J Ambient Intell Human Comput 12(5):4957–4965. https://doi.org/10.1007/s12652-020-01935-x

    Article  Google Scholar 

  32. Alarifi A, Tolba A (2019) Optimizing the network energy of cloud assisted internet of things by using the adaptive neural learning approach in wireless sensor networks. Comput Ind 106:133–141. https://doi.org/10.1016/j.compind.2019.01.004

    Article  Google Scholar 

  33. Yarinezhad R, Azizi S (2021) An energy-efficient routing protocol for the Internet of Things networks based on geographical location and link quality. Comput Netw 193:108116. https://doi.org/10.1016/j.comnet.2021.108116

    Article  Google Scholar 

  34. Zhao Z, Xu K, Hui G, Hu L (2018) An energy-efficient clustering routing protocol for wireless sensor networks based on AGNES with balanced energy consumption optimization. Sensors 18(11):3938. https://doi.org/10.3390/s18113938

    Article  Google Scholar 

  35. Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N (2021) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Human Comput 1–9. https://doi.org/10.1007/s12652-020-02623-6

    Article  Google Scholar 

  36. Raveendran AP, Alzubi JA, Sekaran R, Ramachandran M (2022) A high performance scalable fuzzy based modified Asymmetric Heterogene Multiprocessor System on Chip (AHt-MPSOC) reconfigurable architecture. J Intell Fuzzy Syst 42(2):647–658. https://doi.org/10.3233/JIFS-189737

    Article  Google Scholar 

  37. Babu MV, Alzubi JA, Sekaran R, Patan R, Ramachandran M, Gupta D (2021) An improved IDAF-FIT clustering based ASLPP-RR routing with secure data aggregation in wireless sensor network. Mobile Netw Appl 26(3):1059–1067. https://doi.org/10.1007/s11036-020-01664-7

    Article  Google Scholar 

  38. Alzubi JA (2021) Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks. Trans Emerg Telecommun Technol 32(7):e4069. https://doi.org/10.1002/ett.4069

    Article  Google Scholar 

  39. Prasad RK, Madhu S, Ramotra P, Edla DR (2021) Firework inspired load balancing approach for wireless sensor networks. Wirel Netw 27(6):4111–4122. https://doi.org/10.1007/s11276-021-02710-2

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Dr. K. N. Modi University, Newai, Rajasthan, India

    Poonam Tiwari & Sandeep Kumar Gupta

  2. Electronics and Communication Engineering, Govt. Engineering College, Bharatpur, Rajasthan, India

    Aruna Pathak

Authors
  1. Poonam Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandeep Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aruna Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aruna Pathak.

Ethics declarations

Ethical approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Informed consent

For this type of study, formal consent is not required.

Conflict of interest

The authors declare that they have no potential conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tiwari, P., Gupta, S.K. & Pathak, A. Field-clustering with sleep awake mechanism with fuzzy in wireless sensor network. Peer-to-Peer Netw. Appl. 16, 126–141 (2023). https://doi.org/10.1007/s12083-022-01384-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-022-01384-7

Keywords

Search

Navigation