Advertisement

Wireless Networks

, Volume 25, Issue 1, pp 429–438 | Cite as

A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks

  • Siguang ChenEmail author
  • Jincheng Liu
  • Kun Wang
  • Meng Wu
Article
  • 136 Downloads

Abstract

How to reduce the number of transmissions or prolong the lifetime of wireless sensor networks significantly has become a great challenge. Based on the spatio-temporal correlations of sensory data, in this paper, we propose a hierarchical adaptive spatio-temporal data compression (HASDC) scheme to address this issue. The proposed compression scheme explores the temporal correlation of original sensory data by employing the discrete cosine transform and adaptive threshold compression algorithm (ATCA). And then, the cluster head node explores the spatial correlation among the compressed temporal readings by utilizing discrete wavelet transform (DWT) and ATCA. The HASDC scheme obtains better recovery quality and compression ratio by combining data sorting, ATCA and spatio-temporal compression concept. At the same time, according to the correlation of sensory data and the adaptive threshold value, the HASDC scheme can adjust the compression ratio adaptively, thus it’s applicable to different physical scenarios. Finally, the simulation results confirm that the transformed coefficients are more concentrated than the ones without introducing DWT, and the proposed scheme outperforms other spatio-temporal schemes in terms of compression and recovery performances.

Keywords

Wireless sensor networks Hierarchical network Data sorting Spatio-temporal compression Wavelet transform Discrete cosine transform 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61572262, 61771258), the Six Talented Eminence Foundation of Jiangsu Province (No. XYDXXJS-044), the Natural Science Foundation of Jiangsu Province (No. BK20151507), the 1311 Talents Plan of NUPT, the Scientific Research Foundation of NUPT (No. NY217057) and the Natural Science Foundation for Colleges and Universities in Jiangsu Province (No. 16KJB520034).

References

  1. 1.
    Yang, X., Tao, X., Dutkiewicz, E., et al. (2013). Energy-efficient distributed data storage for wireless sensor networks based on compressed sensing and network coding. IEEE Transactions on Wireless Communications, 12(10), 5087–5099.CrossRefGoogle Scholar
  2. 2.
    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.CrossRefGoogle Scholar
  3. 3.
    Douak, F., Benzid, R., & Benoudijit, N. (2011). Color image compression algorithm based in the DCT transform combined to an adaptive block scanning. AEU-International Journal of Electronics and Communications, 65(1), 16–26.CrossRefGoogle Scholar
  4. 4.
    Dang, T., Bulusu, N. & Feng, W. C. (2007). RIDA: A robust information driven data compression architecture for irregular wireless sensor networks. In Proceedings of 4th European conference on wireless sensor networks (EWSN) (pp. 133–149).Google Scholar
  5. 5.
    Nguyen, M. T., & Teague, K. A. (2015). Distributed DCT-based data compression in clustered wireless sensor networks. In Proceedings of 11th international conference on the design of reliable communication networks (DRCN) (pp. 255–258).Google Scholar
  6. 6.
    Chen, S., Liu, J., & Wu, M., et al. (2016). DCT-based adaptive data compression in wireless sensor networks. In Proceedings of 25th international conference on computer communication and networks (ICCCN) (pp. 1–5).Google Scholar
  7. 7.
    Chen, S., Liu, J., Wang, K., et al. (2016). Data sorting-based adaptive spatial compression in wireless sensor networks. KSII Transactions on Internet and Information Systems, 10(8), 3641–3655.Google Scholar
  8. 8.
    Nabaee, M., & Labeau, F. (2014). Quantized network coding for correlated sources. EURASIP Journal on Wireless Communications and Networking, 2014, 1–17.CrossRefGoogle Scholar
  9. 9.
    Wang, Y. C., Hsieh, Y. Y., & Tseng, Y. C. (2008). Compression and storage schemes in a sensor network with spatial and temporal coding techniques. In Proceedings of IEEE 67th vehicular technology conference (VTC) (pp. 148–152).Google Scholar
  10. 10.
    Kong, L., Xia, M., Liu, X., et al. (2014). Data loss and reconstruction in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(11), 2818–2828.CrossRefGoogle Scholar
  11. 11.
    Lee, D., & Choi, J. (2015). Learning compressive sensing models for big spatio-temporal data. In Proceedings of SIAM international conference on data mining (pp. 667–675).Google Scholar
  12. 12.
    Gong, B., Cheng, P., Liu, N., et al. (2015). Spatiotemporal compressive network coding for energy-efficient distributed data storage in wireless sensor networks. IEEE Communications Letters, 19(5), 803–806.CrossRefGoogle Scholar
  13. 13.
    Quan, L., Xiao, S., Xue, X., et al. (2016). Neighbor-aided spatio-temporal compressive data gathering in wireless sensor networks. IEEE Communications Letters, 20(3), 578–581.CrossRefGoogle Scholar
  14. 14.
    Chen, S., Wu, M., Wang, K., et al. (2014). Compressive network coding for error control in wireless sensor networks. Wireless Networks, 20(8), 2605–2615.CrossRefGoogle Scholar
  15. 15.
    Chen, S., Zhao, C., & Wu, M., et al. (2015). Cluster spatio-temporal compression design for wireless sensor networks. In Proceedings of international conference on computer communication and networks (ICCCN) (pp. 1–6).Google Scholar
  16. 16.
    Xu, X., Ansan, R., Khokhar, A., et al. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks, 11(3), 1–5.CrossRefGoogle Scholar
  17. 17.
    Gao, Z., Dai, L., Dai, W., et al. (2016). Structured compressive sensing-based spatial-temporal joint channel estimation for FDD massive MIMO. IEEE Transaction on Communications, 64(2), 601–617.CrossRefGoogle Scholar
  18. 18.
    Chen, S., Zhao, C., Wu, M., et al. (2016). Compressive network coding for wireless sensor networks: Spatio-temporal coding and optimization design. Computer Networks, 108, 345–356.CrossRefGoogle Scholar
  19. 19.
    Gonzalez, R., & Woods, R. (2008). Digital image processing (3rd ed.). Upper Saddle River, NJ: Prentice Hall Press.Google Scholar
  20. 20.
    Tedmori, R. S., & Al-Najdawi, N. (2014). Image cryptographic algorithm based on the Haar wavelet transform. Information Sciences, 269(11), 21–34.MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Krishnan, A. M., & Kumar, P. G. (2016). An effective clustering approach with data aggregation using multiple mobile sinks for heterogeneous WSN. Wireless Personal Communications, 90(2), 423–434.CrossRefGoogle Scholar
  22. 22.
    Izadi, D., Abawajy, J., & Ghanavati, S. (2015). An alternative clustering scheme in WSN. IEEE Sensors Journal, 15(7), 4148–4155.CrossRefGoogle Scholar
  23. 23.
    Astranchan, O. (2003). Bubble sort: an archaeological algorithmic analysis. ACM SIGCSE Bulletin, 35(1), 1–5.CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Masoum, A., Meratnia, N., & Havinga, P. J. M. (2013). A distributed compressive sensing technique for data gathering in wireless sensor networks. Procedia Computer Science, 21, 207–216.CrossRefGoogle Scholar
  26. 26.
    Wang, S., Ruby, R., Leung, V. C., et al. (2016). A low-complexity power allocation strategy to minimize sum-source-power for multi-user single-AF-relay networks. IEEE Transactions on Communications, 64(8), 3275–3283.CrossRefGoogle Scholar
  27. 27.
    Wang, S., Ruby, R., Leung, V. C., et al. (2016). Energy-efficient power allocation for multi-user single-AF-relay underlay cognitive radio networks. Computer Networks, 103, 115–128.CrossRefGoogle Scholar
  28. 28.
    Wang, K., Gao, H., Xu, X., et al. (2016). An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks. IEEE Sensors Journal, 16(11), 4051–4062.CrossRefGoogle Scholar
  29. 29.
    Chen, S., Wang, K., Zhao, C., et al. (2017). Accelerated distributed optimization design for reconstruction of big sensory data. IEEE Internet of Things Journal,. doi: 10.1109/JIOT.2017.2709810.Google Scholar
  30. 30.
    Zhang, G., Li, X., Cui, M., et al. (2016). Signal and artificial noise beamforming for secure simultaneous wireless information and power transfer multiple-input multiple-output relaying systems. IET Communications, 10(7), 796–804.CrossRefGoogle Scholar
  31. 31.
    Zhang, G., Li, Q., Zhang, Q., et al. (2015). Signal-to-interference-plus-noise ratio-based multi-relay beamforming for multi-user multiple-input multiple-output cognitive relay networks with interference from primary network. IET Communications, 9(2), 227–238.CrossRefGoogle Scholar
  32. 32.
    Wang, K., Shao, Y., Shu, L., et al. (2015). LDPA: A local data processing architecture in ambient assisted living communications. IEEE Communications Magazine, 53(1), 56–63.CrossRefGoogle Scholar
  33. 33.
    Wang, K., Shao, Y., Shu, L., et al. (2016). Mobile big data fault-tolerant processing for eHealth networks. IEEE Network, 30(1), 1–7.CrossRefGoogle Scholar
  34. 34.
    Wang, K., Mi, J., & Xu, C., et al. (2016). Real-time load reduction in multimedia big data for mobile internet. ACM Transactions on Multimedia Computing, Communications and Applications, 12(5s), 1–20.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of EducationNanjing University of Posts and TelecommunicationsNanjingChina

Personalised recommendations