Skip to main content

Advertisement

SpringerLink
Log in

Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor network (WSN) is developed as a network of sensors, which engage in sensing and transmitting the data to the sink node. The constraints, such as energy, memory, and bandwidth insist the researchers to develop an efficient method for data transmission in WSN. Accordingly, this paper introduces a data aggregation mechanism based on query processing, Wavelet-based Least Common Ancestor-Sliding window (WLCA-SW). The energy-loss and memory-crisis is well addressed using the proposed WLCA-SW through the successive steps of query processing, duplicate detection, data compression using the wavelet transformation, and data aggregation. The proposed WLCA-SWA is developed with the integration of the weighed sliding window and Least Common Ancestor (LCA), which enables the energy-aware aggregate query processing and de-duplication such that the duplicate records are detected potentially prior to the communication of the sensed data to the sink node. It is prominent that the weighed sliding window is the extension of the existing time-based sliding windows. The effectiveness of the proposed aggregate processing approach is evaluated based on the metrics, such as number of alive nodes, data reduction rate, data-loss percentage, and residual energy, which is found to be 33, 85%, 8.222%, and 0.0610 J at the end of 1000 rounds using 150 nodes for analysis. Moreover, the proposed method has the minimum aggregation error of 0.03, when the analysis is performed using 50 nodes.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Min, J.-K., Ng, R. T., & Shim, K. (2015). Aggregate query processing in the presence of duplicates in wireless sensor networks. Information Sciences, 297, 1–20.

    Article  MathSciNet  Google Scholar 

  2. Ghosal, A., Halder, S., & DasBit, S. (2012). A dynamic TDMA based scheme for securing query processing in WSN. Wireless Networks, 18(2), 165–184.

    Article  Google Scholar 

  3. Brayner, A., Lopes, A., Meira, D., Vasconcelos, R., & Menezes, R. (2008). An adaptive in-network aggregation operator for query processing in wireless sensor networks. The Journal of Systems and Software, 81(3), 328–342.

    Article  Google Scholar 

  4. da Silva, R. I., Macedo, D. F., & Nogueira, J. M. S. (2014). Spatial query processing in wireless sensor networks—A survey. Information Fusion, 15, 32–43.

    Article  Google Scholar 

  5. Kalpakis, K., & Tang, S. (2010). Maximum lifetime continuous query processing in wireless sensor networks. Ad Hoc Networks, 8(7), 723–741.

    Article  Google Scholar 

  6. Li, G., Guo, L., Gao, X., & Liao, M. (2014). Bloom filter based processing algorithms for the multi-dimensional event query in wireless sensor networks. Journal of Network and Computer Applications, 37, 323–333.

    Article  Google Scholar 

  7. Vinolin, V., & Vinusha, S. (2018). Edge-based image steganography using edge least significant bit (ELSB) technique. Multimedia Research, 1(1), 9–16.

    Google Scholar 

  8. Hejun, W., & Luo, Q. (2010). Adaptive holistic scheduling for query processing in sensor networks. Journal of Parallel and Distributed Computing, 70(6), 657–670.

    Article  Google Scholar 

  9. Liu, L., Qin, X.-L., & Zheng, G.-N. (2012). Reliable spatial window aggregation query processing algorithm in wireless sensor networks. Journal of Network and Computer Applications, 35(5), 1537–1547.

    Article  Google Scholar 

  10. Lee, K.-S., Lee, S.-R., Kim, Y., & Lee, C.-G. (2017). Deep learning–based real-time query processing for wireless sensor network. International Journal of Distributed Sensor Networks, 13(5), 1–10.

    Article  Google Scholar 

  11. Boukerche, A., Mostefaoui, A., & Melkemi, M. (2016). Efficient and robust serial query processing approach for large-scale wireless sensor network applications. Ad Hoc Networks, 47, 82–98.

    Article  Google Scholar 

  12. Rani, R. (2018). Distributed query processing optimization in wireless sensor network using artificial immune system. In B. Mishra, S. Dehuri, B. Panigrahi, A. Nayak, B. Mishra, & H. Das (Eds.), Computational intelligence in sensor networks. Studies in Computational Intelligence (Vol. 776). Berlin: Springer.

    Google Scholar 

  13. Wang, L., Zhenhai, H., & Liu, L. (2019). Privacy-preserving and dynamic spatial range aggregation query processing in wireless sensor networks. In G. Li, J. Yang, J. Gama, J. Natwichai, & Y. Tong (Eds.), Database systems for advanced applications. DASFAA 2019. Lecture notes in computer science (Vol. 11448). Cham: Springer.

    Google Scholar 

  14. Zhu, C., Yang, T., Shu, L., & Nishio, S. (2015). Insights of top-k query in duty-cycled wireless sensor networks. IEEE Transactions on Industrial Electronics, 62(2), 1317–1328.

    Article  Google Scholar 

  15. da Silva, R. I., Macedo, D. F., & Nogueir, J. M. S. (2015). Duty cycle aware spatial query processing in wireless sensor networks. Computer Communications, 41, 240–255.

    Google Scholar 

  16. Brayner, A., Lopes, A., Meira, D., Vasconcelos, R., & Menezes, R. (2007). Toward adaptive query processing in wireless sensor networks. Signal Processing, 87(12), 2911–2933.

    Article  Google Scholar 

  17. Deshpande, A., Guestrin, C., Wei, H., & Madden, S. (2005). Exploiting correlated attributes in acquisitional query processing. In Proceedings of the 21st international conference on data engineering and computer society (pp. 143–154).

  18. Xu, Y., Lee, W.-C., Xu, J., & Mitchell, G. (2006). Processing window queries in wireless sensor networks. In Proceedings of IEEE 22nd international conference on data engineering and computer society (pp. 70–80).

  19. Ye, M., Lee, W.-C., Lee, D., & Liu, X. (2013). Distributed processing of probabilistic top-k queries in wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering, 25(1), 76–91.

    Article  Google Scholar 

  20. Miticia, M., Onderwater, M., & de Graafa, M. (2015). Optimal query assignment for wireless sensor networks. International Journal of Electronics and Communications, 69(8), 1102–1112.

    Article  Google Scholar 

  21. Mohanasundaram, R., & Periasamy, P. S. (2015). Clustering based optimal data storage strategy using hybrid swarm intelligence in WSN. Wireless Personal Communications, 85(3), 1381–1397.

    Article  Google Scholar 

  22. Ghosal, A., & DasBit, S. (2015). A lightweight security scheme for query processing in clustered wireless sensor networks. Computers & Electrical Engineering, 41, 240–255.

    Article  Google Scholar 

  23. Brayner, A., Coelho, A. L. V., Marinho, K., Holanda, R., & Castro, W. (2014). On query processing in wireless sensor networks using classes of quality of queries. Information Fusion, 15, 44–55.

    Article  Google Scholar 

  24. Belfkih, A., Duvallet, C., Amanton, L., & Sadeg, B. (2015). A new query processing model for maintaining data temporal consistency in wireless sensor networks. In Proceedings of IEEE international conference on intelligent sensors, sensor networks and information processing (pp. 1–6).

  25. Ganjewara, P., Barani, S., & Wagh, S. J. (2019). A hierarchical fractional LMS prediction method for data reduction in a wireless sensor network. Ad Hoc Networks, 87, 113–127.

    Article  Google Scholar 

  26. Lee, C.-H., Chung, C.-W., & Chun, S.-J. (2010). Effective processing of continuous group-by aggregate queries in sensor networks. The Journal of Systems and Software, 83(12), 2627–2641.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IKG Punjab Technical University, Jalandhar, Punjab, 144008, India

    Reeta Bhardwaj

  2. DAVIET, Jalandhar, India

    Dinesh Kumar

Authors
  1. Reeta Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dinesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reeta Bhardwaj.

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

Bhardwaj, R., Kumar, D. Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network. Wireless Pers Commun 117, 1627–1643 (2021). https://doi.org/10.1007/s11277-020-07938-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07938-3

Keywords

Search

Navigation