Skip to main content
Springer Nature Link
Log in

Balance: depolarized intelligent sensing system with multi-angle energy-saving optimized control model

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

A simulation model of balanced energy consumption for wireless sensor networks (WSNs) is proposed to solve the uneven energy consumption problem in WSN that leads to the rapid death of some nodes and the occurrence of blind spots. The model consists of five parts: a hierarchical model, an energy consumption model, a cluster filtration model, a data transmission model, and a cluster model. It forms a complete scheme that can effectively solve the problems of unreasonable cluster head selection, uneven clustering, and poor network robustness in WSN networking by combining energy consumption depolarization strategies to fill the gap of set optimization scheme. A clustering method is proposed to equalize node load, and an adaptive two-cluster model is used in accordance with node location to equalize network energy consumption. The simulation results show that the proposed energy consumption model can significantly improve the overall performance of the network. The network lifetime is extended by about 120%, the total data transmission per unit of energy is improved by 51% on average, the redundant data generation is reduced by 44.2%, and the localization tracking success rate is reduced by only 2.14%.

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

Similar content being viewed by others

References

  1. Abbasian Dehkordi S, Farajzadeh K, Rezazadeh J et al (2020) A survey on data aggregation techniques in IoT sensor networks. Wireless Netw 26:1243–1263. https://doi.org/10.1007/s11276-019-02142-z

    Article  Google Scholar 

  2. Famila S, Jawahar A, Sariga A, Shankar K (2020) Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments. Peer-to-Peer Networking and Applications 13(4):1071–1079. https://doi.org/10.1007/s12083-019-00805-4

    Article  Google Scholar 

  3. Marques B, Ricardo M (2017) Energy-efficient node selection in application-driven WSN. Wireless Netw 23:889–918. https://doi.org/10.1007/s11276-016-1194-2

    Article  Google Scholar 

  4. Heinzelman W R, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks[C]// hicss. IEEE Computer Society, https://doi.org/10.1109/HICSS.2000.926982

  5. Handy MJ, Haase M, Timmermann D (2002) Low energy adaptive clustering hierarchy with deterministic cluster-head selection[C]// Mobile and wireless communications network, 2002. 4th international workshop on. IEEE, 10.1109/M WCN.2002.1045790

  6. Sivakumar P, Radhika M (2018) Performance analysis of LEACH-GA over LEACH and LEACH-C in WSN[J]. Procedia Computer Science 125:248–256. https://doi.org/10.1016/j.procs.2017.12.034

    Article  Google Scholar 

  7. Shi S, Liu X, Gu X (2012) An energy-efficiency optimized LEACH-C for wireless sensor networks. 7th international conference on communications and networking in China, Kunming, China, 487–492, https://doi.org/10.1109/ChinaCom.2012.6417532.

  8. Arjunan S, Pothula S (2019) A survey on unequal clustering protocols in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences 31(3):304–317. https://doi.org/10.1016/j.jksuci.2017.03.006

    Article  Google Scholar 

  9. Lu X, Chen R, Liu J, et al. (2018) Square partition-based node scheduling algorithm for wireless passive sensor networks[J]. International Journal of Communication Systems e3531. https://doi.org/10.1002/dac.3531.

  10. Baoyu WU, Peng W, Gaofeng W, et al. (2019) Energy grading charging strategy for wireless rechargeable sensor network[J]. Chinese Journal of Sensors and Actuators, http://en.cnki.com.cn/Article_en/ CJFDTotal-CGJS201908021.htm.

  11. Zhang J, Yan R (2019) Centralized energy-efficient clustering routing protocol for mobile nodes in wireless sensor networks. IEEE Communications Letters PP.99:1–1. https://doi.org/10.1109/LCOMM.2019.2917193.

  12. Singh Sunil Kumar, Kumar Prabhat, Singh Jyoti Prakash (2017) A survey on successors of LEACH protocol. Ieee Access 5:4298–4328. https://doi.org/10.1109/ACCESS.2017.2666082

    Article  Google Scholar 

  13. Song J, Ma ZY, Xu S, Definition and derivation of algorithmic energy consumption complexity [J]. J Comput Sci, 2018, 041(003):709–723. https://doi.org/10.11897/SP.J.1016.2018.00709

  14. Zhang Y, Liu TT, Zhang HG et al. (2020) LEACH-R: LEACH relay with cache strategy for mobile robot swarms[J]. IEEE Wirel Commun Lett https://doi.org/10.1109/LWC.2020.3033039

  15. Aierken N, Gagliardi R, Mostarda L, et al. (2015) RUHEED-rotated unequal clustering algorithm for wireless sensor networks[C]//2015 IEEE 29th international conference on advanced information networking and applications workshops. IEEE, 170–174. https://doi.org/10.1109/WAINA.2015.86

  16. Jin Z, Kim DY, Cho J et al (2015) An analysis on optimal cluster ratio in cluster-based wireless sensor networks[J]. IEEE Sens J 15(11):1–1. https://doi.org/10.1109/JSEN.2015.2459374

    Article  Google Scholar 

  17. Pei E, Pei J, Liu S et al (2019) A heterogeneous nodes-based low energy adaptive clustering hierarchy in cognitive radio sensor network[J]. IEEE Access 7:132010–132026. https://doi.org/10.1109/ACCESS.2019.2940726

    Article  Google Scholar 

  18. Sarkar A, Senthil Murugan T (2017) Cluster head selection for energy efficient and delay-less routing in wireless sensor network[J]. Wireless Networks, https://link.springer.com/content/pdf/https://doi.org/10.1007/s11276-017-1558-2.pdf.

  19. Ghosh N, Banerjee I, Sherratt RS (2019) On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network[J]. Wireless Netw 25(4):1829–1845. https://doi.org/10.1007/s11276-017-1635-6

    Article  Google Scholar 

  20. Khan FA, Farooqi AH, Derhab A (2019) A comprehensive security analysis of LEACH++ clustering protocol for wireless sensor networks[J]. Journal of supercomputing 75(4):2221–2242. https://doi.org/10.1007/s11227-018-2680-3

    Article  Google Scholar 

  21. Nayebia, Sarbaz-Azad H (2011) Performance modeling of the LEACH protocol for mobile wireless sensor networks[J]. J Parallel Distrib Comput 71(6):812-821. https://doi.org/10.1016/j.jpdc.2011.02.004

  22. Wu X, Zeng X, Fang B (2018) An efficient energy-aware and game-theory-based clustering protocol for wireless sensor networks[J]. ICE Transactions on Communications E101.B(3):709–722. https://doi.org/10.1587/transcom.2017EBP3195

  23. Hemavathi N, Meenalochani M, Sudha S (2020) Influence of received signal strength on prediction of cluster head and number of rounds[J]. IEEE Trans Instrum Meas 69(6):3739–3749. https://doi.org/10.1109/TIM.2019.2932652

    Article  Google Scholar 

  24. Abidi W, Ezzedine T (2019) Effective clustering protocol based on network division for heterogeneous wireless sensor networks[J]. Computing 102(1). https://doi.org/10.1007/s00607-019-00757-w.

  25. Jarwan A, Sabbah A, Ibnkahla M (2019) Data transmission reduction schemes in WSN for efficient IoT systems[J]. IEEE J Sel Areas Commun 37(6):1307–1324. https://doi.org/10.1109/JSAC.2019.2904357

    Article  Google Scholar 

  26. Deng J, Han YS, Heinzelman WB et al (2005) Scheduling sleeping nodes in high density cluster-based sensor networks[J]. Mob Networks Appl 10(6):825–835. https://doi.org/10.1007/s11036-005-4441-9

    Article  Google Scholar 

  27. Weiqi C, Han C, Quansheng G, et al. (2018) Evolutionary sleep scheduling in software-defined networks[J]. IEEE Access, PP:1–1. https://doi.org/10.1109/ACCESS.2018.2841985

  28. Wan R, Xiong N, Loc NT (2018) An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks[J]. Human-Centric Computing and Information Sciences 8(1). https://doi.org/10.1186/s13673-018-0141-x.

  29. Li W, Ren XL (2019) Dormancy scheduling strategy for wireless sensor network nodes based on data similarity[J]. Journal of Sensor Technology 32(12):1881–1888. https://doi.org/10.3969/j.issn.1004-1699.2019.12.019

    Article  Google Scholar 

  30. Gupta N, Kumar N, Jain S (2016) Coverage problem in wireless sensor networks: a survey[C]//2016 international conference on signal processing, communication, power and embedded system (SCOPES). IEEE: 1742–1749. https://doi.org/10.1109/SCOPES.2016.7955741.

  31. Zhang S, Li J, Yang Q, et al. (2019) Residual-energy aware LEACH approach for wireless sensor networks[C]//2019 eleventh international conference on ubiquitous and future networks (ICUFN). IEEE 413–418. https://doi.org/10.1109/ICUFN.2019.8806186.

  32. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks[J]. IEEE Trans Wireless Commun 1(4):660–670. https://doi.org/10.1109/TWC.2002.804190

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China (62176067); National Key Research and Development Program of China (SQ2020YFF0416833); Scientific and Technological Planning Project of Guangzhou (201903010041, 202103000040); Key Project of Guangdong Province Basic Research Foundation (2020B1515120095); and Project Supported by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019).

Author information

Authors and Affiliations

  1. Guangdong Polytechnic Normal University, No.293, Zhongshan Avenue West, Tianhe District, Guangzhou, China

    Xu Lu, Kezhou Chen & Rongjun Chen

Authors
  1. Xu Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kezhou Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Rongjun Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xu Lu.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

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

Lu, X., Chen, K. & Chen, R. Balance: depolarized intelligent sensing system with multi-angle energy-saving optimized control model. Ann. Telecommun. 77, 835–846 (2022). https://doi.org/10.1007/s12243-022-00913-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-022-00913-y

Keywords