Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for Data Aggregation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In modern days, Wireless Sensor Network (WSN) is an emerging research area in which various resources constrained sensor nodes are associated by wireless radio with minimum bandwidth. Generally, WSN includes more devices, which are microsensors, wireless radios, and microprocessors. The sensor node in WSN has capable of sensing, self-controlling, offering wireless communication and estimation processing. The information attained by sensors is accumulated resourceful devices, named actuator nodes or central unit, termed as Sink node or base station. The sink node assists in transferring data gathered from a network and vice versa. Therefore, several WSN applications need data collection from sensor nodes based on sink nodes. A productive approach is needed for obtaining data efficiency through decreasing nodal energy consumption. In this research, a Hierarchical Fractional quantized kernel least mean square (HFQKLMS) filter was devised for data aggregation in WSN. Moreover, the HFQKLMS technique was devised by combining Kernel Least Mean Square and Hierarchical Fractional Bidirectional Least-Mean-Square (HFBLMS) approach. Besides, data redundancy is attained by broadcasting the required data using data predicted at the sink node. Besides, the performance of the developed HFQKLMS technique for data aggregation obtained less energy consumption of 0.021 J, and a prediction error of 7.45 based on 100 nodes in the localization database.

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

Similar content being viewed by others

References

  1. Leso, L., Conti, L., Rossi, G., & Barbari, M. (2018). Criteria of design for deconstruction applied to dairy cows housing: A case study in Italy. Agronomy Research, 16(3), 794–805

    Google Scholar 

  2. Conti, L., Bartolozzi S., Racanelli, V., Sorbetti Guerri, F., Iacobelli, S. (2018). Alarm guard systems for the prevention of damage produced by ungulates in a chestnut grove of Middle Italy.

  3. He, D., Zeadally, S., Kumar, N., & Lee, J. H. (2016). Anonymous authentication for wireless body area networks with provable security. IEEE Systems Journal, 11(4), 2590–2601

    Article  Google Scholar 

  4. Cui, J., Shao, L., Zhong, H., Yan, Xu., & Liu, Lu. (2018). Data aggregation with end-to-end confidentiality and integrity for large-scale wireless sensor networks. Peer-to-Peer Networking and Applications, 11(5), 1022–1037

    Article  Google Scholar 

  5. Anandkumar, M. (2020) Multicast routing in WSN using bat algorithm with genetic operators for IoT applications. Journal of Networking and Communication Systems, 3(2).

  6. Al Maqbali, B. (2020). Sensor activation in WSN using improved cuckoo search and squirrel search algorithm. Journal of Networking and Communication Systems, 3(2).

  7. Hu, S., Liu, L., Fang, L., Zhou, F., & Ye, R. (2019). A novel energy-efficient and privacy-preserving data aggregation for WSNs. IEEE Access, 8, 802–813

    Article  Google Scholar 

  8. Elshrkawey, M., Elsherif, S. M., & Wahed, M. E. (2018). An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 30(2), 259–267

    Article  Google Scholar 

  9. Babu, M. V., Alzubi, J. A., Sekaran, R., Patan, R., Ramachandran, M., & Gupta, D. (2020). An Improved IDAF-FIT clustering based ASLPP-RR routing with secure data aggregation in wireless sensor network. Mobile Networks and Applications, 7, 1–9

    Google Scholar 

  10. Polepally, V., & Chatrapati, K. S. (2019). Dragonfly optimization and constraint measure-based load balancing in cloud computing. Cluster Computing, 22(1), 1099–1111

    Article  Google Scholar 

  11. Polepally, V., & Chatrapati, K. S. (2018). DEGSA-VMM: Dragonfly-based exponential gravitational search algorithm to VMM strategy for load balancing in cloud computing. Kybernetes, 47(6), 1138–1157

    Article  Google Scholar 

  12. Polepally, V., & Chatrapati, K. S. (2018). Exponential gravitational search algorithm-based VM migration strategy for load balancing in cloud computing. International Journal of Modeling, Simulation, and Scientific Computing, 9(1), 1850002

    Article  Google Scholar 

  13. Apavatjrut, A., Znaidi, W., Fraboulet, A., Goursaud, C., Lauradoux, C., Minier, M. (2010). Energy friendly integrity for network coding in wireless sensor networks. In Proceedings of 2010 Fourth International Conference on Network and System Security, (pp. 223–230).

  14. Lu, Y., & Sun, N. (2018). A resilient data aggregation method based on spatio-temporal correlation for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2018(1), 1–9

    Article  Google Scholar 

  15. Bongale, A. M., Nirmala, C. R. and Bongale, A. M. (2020). Energy efficient intra-cluster data aggregation technique for wireless sensor network. International Journal of Information Technology.

  16. Stojmenovic, I. (2014). Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems. IEEE Internet of Things Journal, 1(2), 122–128

    Article  Google Scholar 

  17. Zhang, P., Wang, J., Guo, K., Fan, Wu., & Min, G. (2018). Multi-functional secure data aggregation schemes for WSNs. Ad Hoc Networks, 69, 86–99

    Article  Google Scholar 

  18. Jiang M, Fu A. W., Wong R. C. (2015) Exact top-k nearest keyword search in large networks. In Proceedings of the 2015 ACM SIGMOD international conference on management of data (pp. 393–404).

  19. Boudia, O. R., Senouci, S. M., & Feham, M. (2015). A novel secure aggregation scheme for wireless sensor networks using stateful public key cryptography. Ad Hoc Networks, 32, 98–113

    Article  Google Scholar 

  20. Mohanty, P., & Kabat, M. R. (2014). A hierarchical energy efficient reliable transport protocol for wireless sensor networks. Ain Shams Engineering Journal, 5(4), 1141–1155

    Article  Google Scholar 

  21. Chao, C. M., & Hsiao, T. Y. (2014). Design of structure-free and energy balanced data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 37, 229–239

    Article  Google Scholar 

  22. Chen, C. M., Lin, Y. H., Lin, Y. C., & Sun, H. M. (2011). RCDA: Recoverable concealed data aggregation for data integrity in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(4), 727–734

    Article  Google Scholar 

  23. Song, H., Sui, S., Han, Q., Zhang, H., & Yang, Z. (2020). Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks. International Journal of Distributed Sensor Networks, 16(3), 1550147720912958

    Article  Google Scholar 

  24. Lin, Y. H., Chang, S. Y., & Sun, H. M. (2012). CDAMA: Concealed data aggregation scheme for multiple applications in wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering, 25(7), 1471–1483

    Article  Google Scholar 

  25. Bhushan, S., Kumar, M., Kumar, P., Stephan, T., Shankar, A., & Liu, P. (2021). FAJIT: A fuzzy-based data aggregation technique for energy efficiency in wireless sensor network. Complex & Intelligent Systems, 1-11.

  26. Shim, K. A., & Park, C. M. (2014). A secure data aggregation scheme based on appropriate cryptographic primitives in heterogeneous wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2128–2139

    Article  Google Scholar 

  27. Nath, S., Gibbons, P. B., Seshan, S., & Anderson, Z. (2008). Synopsis diffusion for robust aggregation in sensor networks. ACM Transactions on Sensor Networks (TOSN), 4(2), 1–40

    Article  Google Scholar 

  28. Razaque, A., & Rizvi, S. S. (2017). Secure data aggregation using access control and authentication for wireless sensor networks. Computers & Security, 70, 532–545

    Article  Google Scholar 

  29. Tan, L., & Wu, M. (2015). Data reduction in wireless sensor networks: A hierarchical LMS prediction approach. IEEE Sensors Journal, 16(6), 1708–1715

    Article  Google Scholar 

  30. Zhou, L., Ge, C., Hu, S., & Su, C. (2019). Energy-efficient and privacy-preserving data aggregation algorithm for wireless sensor networks. IEEE Internet of Things Journal, 7(5), 3948–3957

    Article  Google Scholar 

  31. Zhang, J., Lin, Z., Tsai, P. W., & Xu, L. (2020). Entropy-driven data aggregation method for energy-efficient wireless sensor networks. Information Fusion, 56, 103–113

    Article  Google Scholar 

  32. Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks, 22(5), 1461–1474

    Article  Google Scholar 

  33. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670

    Article  Google Scholar 

  34. Ganjewar, P. D., Barani, S., & Wagh, S. J. (2018). HFBLMS: Hierarchical fractional bidirectional least-mean-square prediction method for data reduction in wireless sensor network. International Journal of Modeling, Simulation, and Scientific Computing, 9(2), 1850020

    Article  Google Scholar 

  35. Zheng, Y., Wang, S., Feng, J., & Tse, C. K. (2016). A modified quantized kernel least mean square algorithm for prediction of chaotic time series. Digital Signal Processing, 48, 130–136

    Article  MathSciNet  Google Scholar 

  36. Bhaladhare P. R., Jinwala D. C. (2014) A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Advances in Computer Engineering.

  37. Yapıcı, Y., & Yılmaz, A. O. (2012). An analysis of the bidirectional LMS algorithm over fast-fading channels. IEEE Transactions on Communications, 60(7), 1759–1764

    Article  Google Scholar 

  38. Air Quality database taken from, https://archive.ics.uci.edu/ml/datasets/Air+Quality. Accessed on March 2021.

  39. Localization Database taken from, https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity. Accessed on March 2021.

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari, India

    S. Ninisha Nels & J. Amar Pratap Singh

Authors
  1. S. Ninisha Nels
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Amar Pratap Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Ninisha Nels.

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

Ninisha Nels, S., Amar Pratap Singh, J. Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for Data Aggregation. Wireless Pers Commun 120, 1171–1192 (2021). https://doi.org/10.1007/s11277-021-08509-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08509-w

Keywords

Search

Navigation