Skip to main content

Advertisement

SpringerLink
Log in

Data-Prediction Model Based on Stepwise Data Regression Method in Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

As the popularity of wireless sensor networks (WSNs) is rapidly expanding across businesses and industries, we must make everything around us more intelligent and informative. In such networks, sensor nodes (SNs) serve as the WSN's eyes, obtaining data about various environments and conditions, while the base station (or Sink) serves as the WSN's brain, analyzing the acquired data and making decisions. However, on the one hand, the large volume of data collected by the SNs consumes the SNs' limited energy and complicates data analysis at the sink for decision making. In this article, we present a data prediction model based on the stepwise data regression method for handling large amounts of data obtained by cluster-based WSNs. The prediction model is built stepwise data regression method that is implemented at both tiers of each cluster: cluster member nodes and Cluster Heads; and is compatible with both homogeneous and heterogeneous network setups. In intracluster data transmissions, the proposed data prediction model employs a two-buffer stepwise data regression method to synchronize the sensed and predicted data intending to reduce the cumulative errors from continuous predictions. The performance of the proposed work is examined by extensive simulations on real sensor data collected from several applications and is also compared with CPMDC (Diwakaran et al. in J Supercomput 75:3302–3316, 2019) and TDPA (Sinha and Lobiyal in Wirel Pers Commun 84:1325–1343, 2015) models. The proposed model proved to be very energy efficient, with improved data prediction accuracy, increased network lifetime, and more successful data predictions while sustaining an acceptable data accuracy, and improved network lifetime when compared with CPMDC (Diwakaran et al. 2019) and TDPA (Sinha and Lobiyal 2015) models. respectively.

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

Similar content being viewed by others

Data Availability

Data will be made available on reasonable request.

References

  1. Lazarescu, M. T. (2013). Design of a WSN platform for long-term environmental monitoring for IoT applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 3(1), 45–54.

    Article  Google Scholar 

  2. Jain, K., & Kumar, A. (2019). An optimal RSSI-based cluster-head selection for sensor networks. International Journal of Adaptive and Innovative Systems, 2(4), 349–361.

    Article  Google Scholar 

  3. Agarwal, A., Jain, K., & Dev, A. (2022). BFL: A buffer based linear filtration method for data aggregation in wireless sensor networks. International Journal of Information Technology, 14(3), 1445–1454.

    Article  Google Scholar 

  4. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.

    Article  Google Scholar 

  5. Jain, K., & Kumar, A. (2021). A lightweight data transmission reduction method based on a dual prediction technique for sensor networks. Transactions on Emerging Telecommunications Technologies, 32(11), e4345.

    Article  Google Scholar 

  6. Agarwal, A., Dev, A., & Jain, K. (2020). Prolonging sensor network lifetime by using energy-efficient cluster-based scheduling. International Journal of Scientific & Technology Research, 9(4). ISSN 2277-8616.

  7. Jain, K., Mehra, P. S., Dwivedi, A. K., & Agarwal, A. (2022). SCADA: scalable cluster-based data aggregation technique for improving network lifetime of wireless sensor networks. The Journal of Supercomputing, 78(11), 1–29.

    Google Scholar 

  8. Khan, M. N., Rahman, H. U., Almaiah, M. A., Khan, M. Z., Khan, A., Raza, M., & Khan, R. (2020). Improving energy efficiency with content-based adaptive and dynamic scheduling in wireless sensor networks. IEEE Access, 8, 176495–176520.

    Article  Google Scholar 

  9. Jain, K., Agarwal, A., & Kumar, A. (2021). A novel data prediction technique based on correlation for data reduction in sensor networks. In Proceedings of International Conference on Artificial Intelligence and Applications (pp. 595–606). Singapore: Springer.

  10. Jain, K., Agarwal, A., & Abraham, A. (2022). A combinational data prediction model for data transmission reduction in wireless sensor networks. IEEE Access, 10, 53468–53480.

    Article  Google Scholar 

  11. Khalaf, O. I., & Abdulsahib, G. M. (2020). Energy efficient routing and reliable data transmission protocol in WSN. International Journal of Advances in Soft Computing and its Applications, 12(3), 45–53.

    Google Scholar 

  12. Faheem, M., Butt, R. A., Raza, B., Ashraf, M. W., Ngadi, M. A., & Gungor, V. C. (2019). Energy efficient and reliable data gathering using internet of software-defined mobile sinks for WSNs-based smart grid applications. Computer Standards & Interfaces, 66, 103341.

    Article  Google Scholar 

  13. Chandnani, N., & Khairnar, C. N. (2022). An analysis of architecture, framework, security and challenging aspects for data aggregation and routing techniques in IoT WSNs. Theoretical Computer Science, 929, 95–113.

    Article  MATH  Google Scholar 

  14. Yun, W. K., & Yoo, S. J. (2021). Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access, 9, 10737–10750.

    Article  Google Scholar 

  15. Jain, K., & Kumar, A. (2022). An innovative framework for balanced cluster‐based data aggregation in sensor networks. International Journal of Communication Systems, 35(13), e5238.

  16. Issariyakul, T., & Hossain, E. (2009). Introduction to network simulator 2 (NS2). In Introduction to network simulator NS2 (pp. 1–18). Boston, MA: Springer.

  17. Madden S. (2004). Intel lab data. web page, intel.

  18. Liang, Y., & Li, Y. (2014). An efficient and robust data compression algorithm in wireless sensor networks. IEEE Communications Letters, 18(3), 439–442.

    Article  Google Scholar 

  19. Harb, H., Makhoul, A., & Couturier, R. (2015). An enhanced K-means and ANOVA-based clustering approach for similarity aggregation in underwater wireless sensor networks. IEEE Sensors Journal, 15(10), 5483–5493.

    Article  Google Scholar 

  20. Dhimal, S., & Sharma, K. (2015). Energy conservation in wireless sensor networks by exploiting inter-node data similarity metrics. International Journal of Energy, Information and Communications, 6(2), 23–32.

    Article  Google Scholar 

  21. Harb, H., Makhoul, A., Laiymani, D., Bazzi, O., & Jaber, A. (2015). An analysis of variance-based methods for data aggregation in periodic sensor networks. In Transactions on large-scale data-and knowledge-centered systems (Vol. 22, pp. 165–183). Berlin, Heidelberg: Springer.

  22. Sinha, A., & Lobiyal, D. K. (2015). Prediction models for energy efficient data aggregation in wireless sensor network. Wireless Personal Communications, 84(2), 1325–1343.

    Article  Google Scholar 

  23. Wu, M., Tan, L., & Xiong, N. (2016). Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Information Sciences, 329, 800–818.

    Article  Google Scholar 

  24. Harb, H., Makhoul, A., & Abou Jaoude, C. (2018). A real-time massive data processing technique for densely distributed sensor networks. IEEE Access, 6, 56551–56561.

    Article  Google Scholar 

  25. Rida, M., Makhoul, A., Harb, H., Laiymani, D., & Barhamgi, M. (2019). EK-means: A new clustering approach for datasets classification in sensor networks. Ad Hoc Networks, 84, 158–169.

    Article  Google Scholar 

  26. Diwakaran, S., Perumal, B., & Vimala Devi, K. (2019). A cluster prediction model-based data collection for energy efficient wireless sensor network. The Journal of Supercomputing, 75(6), 3302–3316.

    Article  Google Scholar 

  27. Harb, H., Makhoul, A., Jaber, A., & Tawbi, S. (2019). Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation. International Journal of Sensor Networks, 29(1), 1–15.

    Article  Google Scholar 

  28. Al-Qurabat, A. K. M., & Idrees, A. K. (2019). Two level data aggregation protocol for prolonging lifetime of periodic sensor networks. Wireless Networks, 25(6), 3623–3641.

    Article  Google Scholar 

  29. Idrees, A. K., Alhussaini, R., & Salman, M. A. (2020). Energy-efficient two-layer data transmission reduction protocol in periodic sensor networks of IoTs. Personal and Ubiquitous Computing, 1–20.

  30. Jain, K., & Kumar, A. (2020). Energy-efficient data-aggregation technique for correlated spatial and temporal data in cluster-based sensor networks. International Journal of Business Data Communications and Networking (IJBDCN), 16(2), 53–68.

    Article  Google Scholar 

  31. Jain, K., & Kumar, A. (2020). An energy-efficient prediction model for data aggregation in sensor network. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5205–5216.

    Article  Google Scholar 

  32. Al-Qurabat, A. K. M., & Kadhum Idrees, A. (2020). Data gathering and aggregation with selective transmission technique to optimize the lifetime of Internet of Things networks. International Journal of Communication Systems, 33(11), e4408.

    Article  Google Scholar 

  33. Jain, K., & Kumar, A. (2021). ST-DAM: Exploiting spatial and temporal correlation for energy-efficient data aggregation method in heterogeneous WSN. International Journal of Wireless and Mobile Computing, 21(3), 285–294.

    Article  Google Scholar 

  34. Agarwal, A., Jain, K., & Dev, A. (2021). Modeling and analysis of data prediction technique based on linear regression model (DP-LRM) for cluster-based sensor networks. International Journal of Ambient Computing and Intelligence (IJACI), 12(4), 98–117.

    Article  Google Scholar 

  35. Gupta, M., & Sinha, A. (2021). Distributed temporal data prediction model for wireless sensor network. Wireless Personal Communications, 119(4), 3699–3717.

    Article  Google Scholar 

  36. Jain, K., & Singh, A. (2022). A two vector data-prediction model for energy-efficient data aggregation in wireless sensor network. Concurrency and Computation: Practice and Experience, 34(11), e6840.

    Article  Google Scholar 

  37. Shagari, N. M., Salleh, R. B., Ahmedy, I., Idris, M. Y. I., Murtaza, G., Ali, U., & Modi, S. (2022). A two-step clustering to minimize redundant transmission in wireless sensor network using sleep-awake mechanism. Wireless Networks, 1–28.

  38. Shafique, A., Asad, M., Aslam, M., Shaukat, S., & Cao, G. (2022). Multi-hop similarity-based-clustering framework for IoT-Oriented Software-Defined wireless sensor networks. IET Wireless Sensor Systems, 12(2), 67–80.

    Article  Google Scholar 

  39. 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 

Download references

Funding

This research received no specific grant from any funding agency.

Author information

Authors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, UK, India

    Khushboo Jain

  2. NIET, Greater Noida, India

    Akansha Singh

Authors
  1. Khushboo Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akansha Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khushboo Jain.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

Human and Animal Rights

This work does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Appendix

Appendix

In this appendix, we enlist all the acronyms used in this paper with their connotations.

ADV

Actual Data Vector

AFM

Adaptive Frame Method

ARIMA

Auto-Regressive Integrated Moving Average

AVM

Adaptive Vector Method

CHs

Cluster Heads

CMNs

Cluster Member Nodes

CPMDC

Cluster Prediction Model-based Data Collection

DA-AFM

Data Aggregation based on Adaptive Frame Method

DPA

Data Prediction Accuracy

DP-LRM

Data Prediction technique based on a Linear Regression Model

DTSR

Data Transmission Suppression Ratio

ECR

Extended Cosine Regression

IBRL

Intel Berkeley Research Laboratory

LEC

Lossless Entropy Compression

LMS

Least Mean Square

MSCSDN

Multi‐hop Similarity‐based‐ Clustering for IoT‐oriented Software Defined Networks

NQR

Normalized Quantile Regression

ODP

On-balance volume indicator-based Data Prediction

OSSLMS

Optimal Step Size LMS

PCA

Principal Component Analysis

PSDP

Percentage of Successful Data Predictions

PSN

Periodic Sensor Network

RD

Relative Deviation

RV

Relative Variation

SCs

Spatial Correlations

SDRM

Stepwise Data Regression Method

S-LEC

Sequential Lossless Entropy Compression

SNs

Sensors Nodes

SOPCH

Sustaining the Optimal Percentage of CHs

ST-DAM

Spatial Temporal Data Aggregation Model

TCs

Temporal Correlations

TDMA

Time-Division Multiple Access

TDPA

Temporal Data Prediction-based Aggregation

TLDA

Two Level Data Aggregation

TLDRT

Two-Level Data Reduction Technique

TLDTR

Two-Layer Data Transmission Reduction

UID

Unique Identifier

WSNs

Wireless Sensor Networks

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

Jain, K., Singh, A. Data-Prediction Model Based on Stepwise Data Regression Method in Wireless Sensor Network. Wireless Pers Commun 128, 2085–2111 (2023). https://doi.org/10.1007/s11277-022-10034-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-10034-3

Keywords

Search

Navigation