Skip to main content

Advertisement

SpringerLink
Log in

Distributed Temporal Data Prediction Model for Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Sensor networks are critical for building smart environments for monitoring various physical and environmental conditions. Several automated tasks involving continuous and critical practically becomes infeasible for humans to perform with precision. Therefore, wireless sensor networks have emerged as the next-generation technology to permeate the technological upgradations into our daily activities. Such intelligent networks, embedded with sensing expertise, however, are severely energy-constrained. Sensor networks have to process and transmit large volumes of data from sensors to sink or base station, requiring a lot of energy consumption. Since energy is a critical resource in the sensor network to drive all its basic functioning, hence, it needs to be efficiently utilized for elongating network lifetime. This makes energy conservation primarily significant in sensor network design, especially at the sensor node level. Our research proposes an On-balance volume indicator-based Data Prediction (ODP) model for predicting the temperature in the sensor network. Our proposed model can be used to predict temperature with a permissible error of tolerance. This helps in reducing excessive power consumption expended in redundant transmissions, thereby increasing the network lifetime. The proposed data prediction model is compared with existing benchmark time series prediction models, namely Linear Regression (LR) and Auto-Regressive Integrated Moving Average (ARIMA). Experimental outcomes endorsed that our proposed prediction model outperformed the existing counterparts in terms of prediction accuracy and reduction in the number of transmissions in clustered architecture.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Matin, M. A., & Islam, M. M. (2012). Overview of wireless sensor network. Wireless Sensor Networks-Technology and Protocols, pp 1–3.

  2. Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad hoc networks, 7(3), 537–568.

    Article  Google Scholar 

  3. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  4. Sinha, A., & Lobiyal, D. K. (2014). Probabilistic data aggregation in information-based clustered sensor network. Wireless Personal Communications, 77(2), 1287–1310.

    Article  Google Scholar 

  5. Sinha, A., & Lobiyal, D. K. (2011). An entropic approach to data aggregation with divergence measure based clustering in sensor network. In International Conference on Advances in Computing and Communications (pp. 132–142). Berlin, Heidelberg: Springer.

  6. Salman, A. G., Kanigoro, B., & Heryadi, Y. (2015, October). Weather forecasting using deep learning techniques. In 2015 international conference on advanced computer science and information systems (ICACSIS) (pp. 281–285). Ieee.

  7. Navadia, S., Yadav, P., Thomas, J., & Shaikh, S. (2017). Weather prediction: a novel approach for measuring and analyzing weather data. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 414–417). IEEE.

  8. Nikam, V. B., & Meshram, B. B. (2013). Modeling rainfall prediction using data mining method: A Bayesian approach. In 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation (pp. 132–136). IEEE.

  9. Bogdanovs, N., Bistrovs, V., Petersons, E., Ipatovs, A., & Belinskis, R. (2018). Weather prediction algorithm based on historical data using kalman filter. In 2018 Advances in Wireless and Optical Communications (RTUWO) (pp. 94–99). IEEE.

  10. Karevan, Z., Mehrkanoon, S., & Suykens, J. A. (2015). Black-box modeling for temperature prediction in weather forecasting. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.

  11. Qiu, T., Zhang, S., Zhou, H., Bai, X., & Liu, P. (2013). Application study of machine learning in lightning forecasting. Information Technology Journal, 12(21), 6031.

    Article  Google Scholar 

  12. Wang, Y. F. (2003). On-demand forecasting of stock prices using a real-time predictor. IEEE Transactions on Knowledge and Data Engineering, 15(4), 1033–1037.

    Article  MathSciNet  Google Scholar 

  13. Hu, Z., Zhu, J., & Tse, K. (2013). Stocks market prediction using support vector machine. In 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 2, pp. 115–118). IEEE.

  14. Somani, P., Talele, S., & Sawant, S. (2014). Stock market prediction using hidden Markov model. In 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (pp. 89–92). IEEE.

  15. Ryota, K., & Tomoharu, N. (2012). Stock market prediction based on interrelated time series data. In 2012 IEEE Symposium on Computers & Informatics (ISCI) (pp. 17–21). IEEE.

  16. Zhao, L., & Wang, L. (2015). Price trend prediction of stock market using outlier data mining algorithm. In 2015 IEEE Fifth International Conference on Big Data and Cloud Computing (pp. 93–98). IEEE.

  17. Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A prediction approach for stock market volatility based on time series data. IEEE Access, 7, 17287–17298.

    Article  Google Scholar 

  18. Zhang, X., Qu, S., Huang, J., Fang, B., & Yu, P. (2018). Stock market prediction via multi-source multiple instance learning. IEEE Access, 6, 50720–50728.

    Article  Google Scholar 

  19. Tsang, W. W. H., & Chong, T. T. L. (2009). Profitability of the on-balance volume indicator. Economics Bulletin, 29(3), 2424–2431.

    Google Scholar 

  20. Farahnakian, F., Liljeberg, P., & Plosila, J. (2013). LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers. In 2013 39th Euromicro conference on software engineering and advanced applications (pp. 357–364). IEEE.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering & Information Technology, Jaypee Institute of Information Technology, Sector 62, Noida, Uttar Pradesh, India

    Meeta Gupta & Adwitiya Sinha

Authors
  1. Meeta Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adwitiya Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adwitiya Sinha.

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

Gupta, M., Sinha, A. Distributed Temporal Data Prediction Model for Wireless Sensor Network. Wireless Pers Commun 119, 3699–3717 (2021). https://doi.org/10.1007/s11277-021-08427-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08427-x

Keywords

Search

Navigation