Skip to main content
Log in

Efficient data routing for agricultural landscapes: ensemble fuzzy crossover based golden jackal approach

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Precision agriculture involves extensive agricultural landscapes with varying terrains and crop types. An energy-efficient routing protocol ensures that data is efficiently transmitted across the entire agricultural area. However, the ability of clustering routing protocol is based on the cluster formation as well as cluster head selection processes. Traditional methods are impractical for such large-scale deployments. In order to conquer the above-mentioned challenges, this paper proposed a novel Ensemble Fuzzy Crossover based Golden Jackal (EFC-GJ) method for enhancing the formation of cluster and cluster heads selection. In the proposed method, the crossover-based Golden Jackal Optimization, Fuzzy c-means Clustering Method, and Ensemble Q-learning are utilized for cluster center initialization, cluster formation, and cluster head selection respectively. The performance evaluation measures such as throughput, jitter, latency, energy consumption, and network lifetime are utilized for the evaluation of the proposed EFC-GJ method and these results are compared with existing methods. The EFC-GJ method attained a PDR of 0.98, throughput of 0.97 Mbps, end-to-end delay of 1.3 s, network lifetime of 5620 rounds, energy consumption of 0.2 mJ, jitter of 0.36 ms, and latency of 1.7 s. The experimental results illustrate the EFC-GJ method’s effectiveness in forming cluster and selecting cluster head.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  1. Wilson, S., Alavi, N., Pouliot, D., Mitchell, G.W.: Similarity between agricultural and natural land covers shapes how biodiversity responds to agricultural expansion at landscape scales. Agric. Ecosyst. Environ.Ecosyst. Environ. 301, 107052 (2020). https://doi.org/10.1016/j.agee.2020.107052

    Article  Google Scholar 

  2. Dhaya, R., Kanthavel, R., Ahilan, A.: RETRACTED ARTICLE: developing an energy-efficient ubiquitous agriculture mobile sensor network-based threshold built-in MAC routing protocol (TBMP). Soft. Comput.Comput. 25(18), 12333–12342 (2021). https://doi.org/10.1007/s00500-021-05927-7

    Article  Google Scholar 

  3. Trappey, A.J., Lin, G.B., Chen, H.K., Chen, M.C.: A comprehensive analysis of global patent landscape for recent R&D in agricultural drone technologies. World Patent Inf. 74, 102216 (2023). https://doi.org/10.1016/j.wpi.2023.102216

    Article  Google Scholar 

  4. Balatsouras, C.P., Karras, A., Karras, C., Karydis, I., Sioutas, S.: WiCHORD+: a scalable, sustainable, and P2P chord-based ecosystem for smart agriculture applications. Sensors 23(23), 9486 (2023). https://doi.org/10.3390/s23239486

    Article  Google Scholar 

  5. Qureshi, K.N., Bashir, M.U., Lloret, J., Leon, A.: Optimized cluster-based dynamic energy-aware routing protocol for wireless sensor networks in agriculture precision. J. Sens. 2020, 1–19 (2020). https://doi.org/10.1155/2020/9040395

    Article  Google Scholar 

  6. Kittur, S., Sundar, K.G.: Of irrigation canals and multifunctional agroforestry: traditional agriculture facilitates Woolly-necked Stork breeding in a north Indian agricultural landscape. Glob. Ecol. Conserv. 30, e01793 (2021). https://doi.org/10.1016/j.gecco.2021.e01793

    Article  Google Scholar 

  7. Sirabahenda, Z., St-Hilaire, A., Courtenay, S.C., Van Den Heuvel, M.R.: Assessment of the effective width of riparian buffer strips to reduce suspended sediment in an agricultural landscape using ANFIS and SWAT models. CATENA 195, 104762 (2020). https://doi.org/10.1016/j.catena.2020.104762

    Article  Google Scholar 

  8. Kwang, J.S., Thaler, E.A., Quirk, B.J., Quarrier, C.L., Larsen, I.J.: A landscape evolution modeling approach for predicting three-dimensional soil organic carbon redistribution in agricultural landscapes. J. Geophys. Res. Biogeosci.Geophys. Res. Biogeosci. 127(2), e2021JG006616 (2022)

    Article  Google Scholar 

  9. Dogra, R., Rani, S., Kavita, Shafi, J., Kim, S., Ijaz, M.F.: ESEERP: Enhanced smart energy efficient routing protocol for internet of things in wireless sensor nodes. Sensors 22(16), 6109 (2022)

    Article  Google Scholar 

  10. Agrawal, H., Dhall, R., Iyer, K.S.S., Chetlapalli, V.: An improved energy efficient system for IoT enabled precision agriculture. J. Ambient. Intell. Humaniz. Comput.Intell. Humaniz. Comput. 11, 2337–2348 (2020)

    Article  Google Scholar 

  11. Li, C., Chen, D., Xie, C., Tang, Y.: Algorithm for wireless sensor networks in ginseng field in precision agriculture. PLoS ONE 17(2), e0263401 (2022)

    Article  Google Scholar 

  12. Xue, X., Shanmugam, R., Palanisamy, S., Khalaf, O.I., Selvaraj, D., Abdulsahib, G.M.: A hybrid cross-layer with harris-hawk-optimization-based efficient routing for wireless sensor networks. Symmetry 15(2), 438 (2023). https://doi.org/10.3390/sym15020438

    Article  Google Scholar 

  13. Sanapala, R.K., Duggirala, S.R.: An optimized energy efficient routing for wireless sensor network using improved spider monkey optimization algorithm. Int. J. Intell. Eng. Syst. (2022). https://doi.org/10.22266/ijies2022.0228.18

    Article  Google Scholar 

  14. Lu, J., Hu, K., Yang, X., Hu, C., Wang, T.: A cluster-tree-based energy-efficient routing protocol for wireless sensor networks with a mobile sink. J. Supercomput.Supercomput. 77, 6078–6104 (2021). https://doi.org/10.1007/s11227-020-03501-w

    Article  Google Scholar 

  15. Pandiyaraju, V., Logambigai, R., Ganapathy, S., Kannan, A.: An energy efficient routing algorithm for WSNs using intelligent fuzzy rules in precision agriculture. Wireless Pers. Commun.Commun. 112, 243–259 (2020)

    Article  Google Scholar 

  16. Wu, M., Li, Z., Chen, J., Min, Q., Lu, T.: A dual cluster-head energy-efficient routing algorithm based on canopy optimization and k-means for WSN. Sensors. 22(24), 9731 (2022). https://doi.org/10.3390/s22249731

    Article  Google Scholar 

  17. Lin, C., Han, G., Qi, X., Du, J., Xu, T., Martinez-Garcia, M.: Energy-optimal data collection for UAV-aided industrial WSN-based agricultural monitoring system: a clustering compressed sampling approach. IEEE Trans. Ind. Informat. (2020).

  18. Pandiyaraju, V., Logambigai, R., Ganapathy, S., Kannan, A.: An energy efficient routing algorithm for WSNs using intelligent fuzzy rules in precision agriculture. Wireless Pers. Commun.Commun. 112, 243–259 (2020). https://doi.org/10.1007/s11277-020-07024-8

    Article  Google Scholar 

  19. Jubair, A.M., Hassan, R., Aman, A.H.M., Sallehudin, H., Al-Mekhlafi, Z.G., Mohammed, B.A., Alsaffar, M.S.: Optimization of clustering in wireless sensor networks: techniques and protocols. Appl. Sci. 11(23), 11448 (2021)

    Article  Google Scholar 

  20. Najjar, I.R., Sadoun, A.M., Fathy, A., Abdallah, A.W., Elaziz, M.A., Elmahdy, M.: Prediction of tribological properties of alumina-coated, silver-reinforced copper nanocomposites using long short-term model combined with golden jackal optimization. Lubricants. 10(11), 277 (2022). https://doi.org/10.3390/lubricants10110277

    Article  Google Scholar 

  21. Geng, J., Wang, H., Su, J., Zheng, X., Sun, X., Wu, X., Zhang, Y.: Coverage optimization of wireless sensor networks with improved golden jackal optimization. In: 2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE. 1–4 (2023)

  22. Rami Reddy, M., Ravi Chandra, M.L., Venkatramana, P., Dilli, R.: Energy-efficient cluster head selection in wireless sensor networks using an improved grey wolf optimization algorithm. Computers 12(2), 35 (2023)

    Article  Google Scholar 

  23. Wang, H., Lin, S., Zhang, J.: Adaptive ensemble q-learning: minimizing estimation bias via error feedback. Adv. Neural. Inf. Process. Syst. 34, 24778–24790 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors agreed on the content of the study. SS, BY, SP and VN collected all the data for analysis. SS agreed on the methodology. SS, BY, SP and VN completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

Corresponding author

Correspondence to S. Sivakumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This article does not contain any studies with human participants.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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 (e.g. a society or other partner) 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

Sivakumar, S., Yamini, B., Palaniswamy, S. et al. Efficient data routing for agricultural landscapes: ensemble fuzzy crossover based golden jackal approach. SIViP 18, 6273–6283 (2024). https://doi.org/10.1007/s11760-024-03313-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03313-y

Keywords

Navigation