Skip to main content
Log in

An Improvement Energy Consumption Policy Using Communication Reduction in Wireless Body Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Since one of the main reasons for improvement network lifetime is communications reduction in collecting vital signs and transmit them to the coordinator. In this paper, it is tried to reduce communications through adaption the sampling rate through individual's discovered pattern, activity prediction and watchdog biosensor. The first, the daily behavior pattern of the individual is identified, then the individual's activities are predicted; if the predicted activity exists in the individual's behavioral pattern, all the sensors are activated to read information with maximum sampling rate. Otherwise, the sensors read information with the minimum sampling rate and the watchdog biosensor is activated to sense and send the vital signs with the maximum sampling rate. The simulation results show that the proposed method improves network traffic by 80% and decreases the energy consumption of the network by four times.

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

Access this article

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
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. We used MIMIC dataset and it cited in main manuscript. And you can get more data here.

References

  1. Habib, C., Makhoul, A., Darazi, R., & Couturier, R. (2019). Health risk assessment and decision-making for patient monitoring and decision-support using wireless body sensor networks. Information Fusion, 47, 10–22.

    Article  Google Scholar 

  2. Azar, J., Habib, C., Darazi, R., Makhoul, A., & Demerjian, J. (2018, October). Using Adaptive sampling and DWT lifting scheme for efficient data reduction in wireless body sensor networks. In 2018 14th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 1–8). IEEE.‏

  3. Habib, C., et al. (2016). Self-adaptive data collection and fusion for health monitoring based on body sensor networks. IEEE Transactions on Industrial Informatics, 12(6), 2342–2352.

    Article  Google Scholar 

  4. White, A. (1987). Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3. Naval Ocean Systems Center, San Diego, Tech. Rep.

  5. Hamilton, P. (2002). Open source ECG analysis. Computers in Cardiology, 2002, IEEE.

  6. Krause, A., et al. (2005). Trading off prediction accuracy and power consumption for context-aware wearable computing. Wearable Computers, 2005. In Proceedings. Ninth IEEE International Symposium on, IEEE.

  7. Chu, D., et al. (2011). Balancing energy, latency and accuracy for mobile sensor data classification. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, ACM.

  8. Yan, Z., et al. (2012). Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In Wearable Computers (ISWC), 2012 16 th International Symposium on IEEE.

  9. Rault, T., et al. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.

    Article  Google Scholar 

  10. Wu, X., et al. (2014). Sparsest random scheduling for compressive data gathering in wireless sensor networks. IEEE Transactions on Wireless Communications, 13(10), 5867–5877.

    Article  Google Scholar 

  11. Luo, C., et al. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on Mobile computing and networking, ACM.

  12. Wang, J., et al. (2012). Data gathering in wireless sensor networks through intelligent compressive sensing. In INFOCOM, 2012 Proceedings IEEE, IEEE.

  13. Luo, C., et al. (2010). Efficient measurement generation and pervasive sparsity for compressive data gathering. IEEE Transactions on Wireless Communications, 9(12), 3728–3738.

    Article  Google Scholar 

  14. Haupt, J., et al. (2008). Compressed sensing for networked data. IEEE Signal Processing Magazine, 25(2), 92–101.

    Article  Google Scholar 

  15. Atallah, L., et al. (2010). Sensor placement for activity detection using wearable accelerometers. In Body Sensor Networks (BSN), 2010 International Conference on, IEEE.

  16. Rachuri, K. K., et al. (2010). EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In Proceedings of the 12th ACM international conference on Ubiquitous computing, ACM.

  17. Lu, H., et al. (2011). Speaker sense: Energy efficient unobtrusive speaker identification on mobile phones. Springer.

    Google Scholar 

  18. Wu, X., et al. (2013). An efficient compressive data gathering routing scheme for large-scale wireless sensor networks. Computers & Electrical Engineering, 39(6), 1935–1964.

    Article  Google Scholar 

  19. Xie, R., & Jia, X. (2014). Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Transactions on Parallel and Distributed Systems, 25(3), 806–815.

    Article  Google Scholar 

  20. Ganesan, M., et al. (2015). A novel based algorithm for the prediction of abnormal heart rate using Bayesian algorithm in the wireless sensor network. In Proceedings of the 2015International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015), ACM.

  21. Wu, F. Y., Yang, K., Duan, R., & Tian, T. (2018). Compressive sampling and reconstruction of acoustic signal in underwater wireless sensor networks. IEEE Sensors Journal, 18(14), 5876–5884.

    Article  Google Scholar 

  22. Sun, P., Wu, L., Wang, Z., Xiao, M., & Wang, Z. (2018). Sparsest random sampling for cluster-based compressive data gathering in wireless sensor networks. IEEE Access, 6, 36383–36394.

    Article  Google Scholar 

  23. Fathy, Y., Barnaghi, P., & Tafazolli, R. (2018). An adaptive method for data reduction in the Internet of Things. In IEEE 4th World Forum on Internet of Things.

  24. Elghers, S., Makhoul, A., & Laiymani, D. (2014). Local emergency detection approach for saving energy in wireless body sensor networks. In Proc. IEEE 10th Int. Conf. Wireless Mobile Comput., Netw. Commun. pp. 585–591

  25. Salim, C., Makhoul, A., Darazi, R., & Couturier, R. (2016). Adaptive sampling algorithms with local emergency detection for energy saving in Wireless Body Sensor Networks. In Network operations and Management Symposium (NOMS), 2016 IEEE/IFIP, IEEE, pp. 745–749.

  26. National Early Warning Score (NEWS), Royal College of Physicians. http://www.rcplondon.ac.uk/resources/national-early-warningscore-news, May 2017.

  27. Fatima, I., et al. (2013). A unified framework for activity recognition-based behavior analysis and action prediction in smart homes. Sensors, 13, 2682–2699.

    Article  Google Scholar 

  28. Scholkopf, B. (1999). Advances in kernel methods: Support vector learning. MIT Press.

    Google Scholar 

  29. Mitchell, T. (1997). Machine learning. McGraw Hill.

    MATH  Google Scholar 

  30. Aizerman, M., Braverman, E., & Rozonoer, L. (1964). Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25, 821–837.

    MATH  Google Scholar 

  31. Fleury, A., Vacher, M., & Noury, N. (2010). SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results. IEEE Transactions on Information Technology in Biomedicine, 14, 274–283.

    Article  Google Scholar 

  32. Fleury, A., Noury, N., & Vacher, M. (2009). Supervised classification of activities of daily living in health smart homes using SVM. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3–6, 2–6.

    Google Scholar 

  33. Li, M., Yang, J., Hao, D., Jia, S. (2009). ECoG recognition of motor imagery based on SVM ensemble. In: Proceedings of the IEEE international conference on robotics and biomimetics (ROBIO), 19–23 Dec 2009, pp 1967–1972

  34. Ayres, J., Gehrke, J., Yiu, T., & Flannick, J. (2002). Sequential pattern mining using bitmaps. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 23–26 July 2002, pp. 429–435

  35. CASAS Smart Home Project. http://casas.wsu.edu/datasets/. September 2018.

  36. Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220.

    Article  Google Scholar 

Download references

Funding

This article related to final thesis in Ph.D and there is not any relationship to other organization. All financial support is related to student (Hamid Mehdi). The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Houman Zarrabi.

Ethics declarations

Conflict of interest

All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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

Mehdi, H., Zarrabi, H., Zadeh, A.K. et al. An Improvement Energy Consumption Policy Using Communication Reduction in Wireless Body Sensor Network. Wireless Pers Commun 125, 3859–3883 (2022). https://doi.org/10.1007/s11277-022-09739-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09739-2

Keywords

Navigation