Skip to main content

Advertisement

Log in

Data Aggregation using Difference transfer for Load Reduction in Periodic Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

One of the basic challenges in wireless sensor networks is energy conservation. Sensor nodes are energy constrained and prudent energy usage is of utmost importance. Data aggregation aims to reduce amount of data communicated across the network without loss in information, thereby reducing the energy costs, and increasing network lifetime. In this paper, we propose a novel, simple and easy to implement method to reduce the amount of periodic data transferred from the sensor nodes to the sink. Instead of sending a set of measures at the end of every time period, we propose sending the first measure, and for every subsequent measure in that time period, we send the difference with respect to first measure. Differences are represented by a group of binary bits. Differences are also chosen in an adaptive manner in order to maintain precision between the data measured at sensor nodes and data reconstructed from binary bit patterns at sink. We evaluated our technique against two real world data-sets with vastly different properties. Results indicate 85–88.5% reduction in amount of data sent and transmission energy.

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

Similar content being viewed by others

References

  1. Stankovic, J. A. (2014). Research directions for the Internet of Things. IEEE Internet of Things Journal, 1(1), 3–9.

    MathSciNet  Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Google Scholar 

  3. Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. B. (2002). Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40–50.

    Google Scholar 

  4. Harb, H., Makhoul, A., Tawil, R., & Jaber, A. (2014). Energy-efficient data aggregation and transfer in periodic sensor networks. IET Wireless Sensor Systems, 4(4), 149–158.

    Google Scholar 

  5. Jesus, P., Baquero, C., & Almeida, P. S. (2014). A survey of distributed data aggregation algorithms. IEEE Communications Survey and Tutorials, 17(1), 381–404.

    Google Scholar 

  6. Krishnamachari, B., Estrin, D., & Wicker, S. (2002). The impact of data aggregation in wireless sensor networks. In ICDCSW: 2002.

  7. Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). Tag: A tiny aggregation service for ad-hoc sensor networks. ACM SIGOPS Operating Systems Review, 36, 131–146.

    Google Scholar 

  8. Ren, F., Zhang, J., Yongwei, W., He, T., Chen, C., & Lin, C. (2013). Attribute-aware data aggregation using potential-based dynamic routing. IEEE Transactions on Parallel and Distributed Systems, 24(5), 881–892.

    Google Scholar 

  9. Wan, S., Zhang, Y., & Chen, J. (2016). On the construction of data aggregation tree with maximizing lifetime in large-scale wireless sensor networks. IEEE Sensors Journal, 16(20), 7433–7440.

    Google Scholar 

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

    Google Scholar 

  11. 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.

    Google Scholar 

  12. Sinha, A., & Lobiyal, D. K. (2013). A multi-level strategy for energy efficient data aggregation in wireless sensor networks. Wireless Personal Communications, 72(2), 1513–1531.

    Google Scholar 

  13. Velmani, R., & Kaarthick, B. (2015). An efficient cluster-tree based data collection scheme for large mobile wireless sensor networks. IEEE Sensors Journal, 15(4), 2377–2390.

    Google Scholar 

  14. Fan, K. W., Liu, S., & Sinha, P. (2006). On the potential of structure-free data aggregation in sensor networks. In IEEE Infocom.

  15. Randhawa, S., & Jain, S. (2017). Data aggregation in wireless sensor networks: Previous research, current status and future directions. Wireless Personal Communications, 97(3), 3355–3425.

    Google Scholar 

  16. Chitnis, L., Dobra, A., & Ranka, S. (2008). Aggregation methods for large-scale sensor networks. ACM Transactions on Sensor Networks, 4(2), 9:1–9:36.

    Google Scholar 

  17. Ganz, F., Puschmann, D., Barnaghi, P., & Carrez, F. (2015). A practical evaluation of information processing and abstraction techniques for the IoT. IEEE Internet of Things Journal, 2(4), 340–354.

    Google Scholar 

  18. Bahi, J. M., Makhoul, A., & Medlej, A. (2014). A two tiers data aggregation scheme for periodic sensor networks. Adhoc and Sensor Wireless Networks, 21(1), 77–100.

    Google Scholar 

  19. Atoui, I., et al. (2016). Tree-based data aggregation approach in periodic sensor networks using correlation matrix and polynomial regression. In IEEE International Conference on CSE, pp. 716–723.

  20. Harb, H., et al. (2017). Comparison of different data aggregation techniques in distributed sensor networks. IEEE Access, 5, 4250–4263.

    Google Scholar 

  21. Madden, S. (2004). Intel Lab Data. http://db.csail.mit.edu/labdata/labdata.html. Accessed 2 June 2004.

  22. Chen, S. X. PM2.5 Data of Five Chinese Cities Data Set. https://archive.ics.uci.edu/ml/datasets/PM2.5+Data+of+Five+Chinese+Cities. Accessed 18 July 2017.

  23. Kabara, J., & Calle, M. (2012). MAC protocols used by wireless sensor networks and a general method of performance evaluation. International Journal of Distributed Sensor Networks, 8(1), 1–11.

    Google Scholar 

  24. Bojthe, Z., et al. (2019). INET Framework. https://inet.omnetpp.org/. Accessed April 2018.

  25. Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 5:1–5:39.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arun Avinash Chauhan.

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

Chauhan, A.A., Udgata, S.K. Data Aggregation using Difference transfer for Load Reduction in Periodic Sensor Networks. Wireless Pers Commun 115, 1507–1524 (2020). https://doi.org/10.1007/s11277-020-07640-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07640-4

Keywords

Navigation