Telecommunication Systems

, Volume 68, Issue 1, pp 79–88 | Cite as

Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing

  • Saeed Mehrjoo
  • Farshad Khunjush


The high number of transmissions in sensor nodes having a limited amount of energy leads to a drastic decrease in the lifetime of wireless sensor networks. For dense sensor networks, the provided data potentially have spatial and temporal correlations. The correlations between the data of the nodes make it possible to utilize compressive sensing theory during the data gathering phase; however, applying this technique leads to some errors during the reconstruction phase. In this paper, a method based on weighted spatial-temporal compressive sensing is proposed to improve the accuracy of the reconstructed data. Simulation results confirm that the reconstruction error of the proposed method is approximately 16 times less than the closest compared method. It should be noted that due to applying weighted spatial-temporal compressive sensing, some extra transmissions are posed to the network. However, considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 12% improvement compared to the closest method in the literature.


Accuracy Compressive sensing Lifetime Wireless sensor networks 


  1. 1.
    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I. F., Vuran, M. C., & Akan, O. B. (2004). On exploiting spatial and temporal correlation in wireless sensor networks. Proceedings of WiOpt, 4, 71–80.Google Scholar
  4. 4.
    Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.CrossRefGoogle Scholar
  5. 5.
    Baraniuk, R. G. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24(4), 118–121.CrossRefGoogle Scholar
  6. 6.
    Candès, E. J., & Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21–30.CrossRefGoogle Scholar
  7. 7.
    Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.CrossRefGoogle Scholar
  8. 8.
    Yildiz, H. U., & Tavli, B. (2015). Prolonging wireless sensor network lifetime by optimal utilization of compressive sensing. In 2015 IEEE globecom workshops (GC Wkshps) (pp. 1–6). IEEE.Google Scholar
  9. 9.
    Zhu, L., Ci, B., Liu, Y., & Chen, Z. D. (2015). Data gathering in wireless sensor networks based on reshuffling cluster compressed sensing. International Journal of Distributed Sensor Networks, 2015, 220.Google Scholar
  10. 10.
    Liu, X.-Y., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.CrossRefGoogle Scholar
  11. 11.
    Nguyen, M. T. (2013). Minimizing energy consumption in random walk routing for wireless sensor networks utilizing compressed sensing. In 2013 8th international conference on system of systems engineering (SoSE) (pp. 297–301). IEEE.Google Scholar
  12. 12.
    Harmany, Z. T., Marcia, R. F., & Willett, R. M. (2011). Spatio-temporal compressed sensing with coded apertures and keyed exposures. arXiv preprint arXiv:1111.7247.
  13. 13.
    Yip, E., et al. (2014). Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors. Medical Physics, 41(8), 082301.CrossRefGoogle Scholar
  14. 14.
    Samsonov, A., Velikina, J., Fleming, J., Schiebler, M., & Field, A. (2010). Accelerated serial MR imaging in multiple sclerosis using baseline scan information. In 18th annual meeting of ISMRM, Stockholm, Sweden (p. 4876). Berkeley, CA: International Society for Magnetic Resonance in Medicine.Google Scholar
  15. 15.
    Vaswani, N., & Lu, W. (2010). Modified-CS: Modifying compressive sensing for problems with partially known support. IEEE Transactions on Signal Processing, 58(9), 4595–4607.CrossRefGoogle Scholar
  16. 16.
    Khajehnejad, M. A., Xu, W., Avestimehr, A. S., Hassibi, B. (2009). Weighted \(\ell \) 1 minimization for sparse recovery with prior information. In 2009 IEEE international symposium on information theory (pp. 483–487). IEEE.Google Scholar
  17. 17.
    Friedlander, M. P., Mansour, H., Saab, R., & Yilmaz, O. (2012). Recovering compressively sampled signals using partial support information. IEEE Transactions on Information Theory, 58(2), 1122–1134.CrossRefGoogle Scholar
  18. 18.
    Luo, C., Wu, F., Sun, J., Chen, C. W. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking (pp. 145–156). ACM.Google Scholar
  19. 19.
    Kalpakis, K., Dasgupta, K., & Namjoshi, P. (2003). Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Computer Networks, 42(6), 697–716.CrossRefGoogle Scholar
  20. 20.
    Althunibat, S., Abu-Al-Aish, A., Shehab, W. F. A., & Alsawalmeh, W. H. (2016). Auction-based data gathering scheme for wireless sensor networks. IEEE Communications Letters, 20(6), 1223–1226.CrossRefGoogle Scholar
  21. 21.
    Zhang, Y., He, S., & Chen, J. (2016). Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Transactions on Networking, 24(3), 1632–1646.CrossRefGoogle Scholar
  22. 22.
    Chen, S., Wu, M., Wang, K., Sun, Z., & Lu, W. (2015). Combining network coding and compressed sensing for error correction in wireless sensor networks. International Journal of Communication Systems, 28(7), 1303–1315.CrossRefGoogle Scholar
  23. 23.
    Chou, C. T., Rana, R., Hu, W. (2009). Energy efficient information collection in wireless sensor networks using adaptive compressive sensing. In IEEE 34th conference on local computer networks, 2009. LCN 2009 (pp. 443–450). IEEE.Google Scholar
  24. 24.
    Liu, Z., Zhang, M., & Cui, J. (2014). An adaptive data collection algorithm based on a Bayesian compressed sensing framework. Sensors, 14(5), 8330–8349.CrossRefGoogle Scholar
  25. 25.
    Quer, G., Masiero, R., Munaretto, D., Rossi, M., Widmer, J., Zorzi, M. (2009). On the interplay between routing and signal representation for compressive sensing in wireless sensor networks. In Information theory and applications workshop (ITA 2009) (pp. 206–215).Google Scholar
  26. 26.
    Chen, S., Zhao, C., Wu, M., Sun, Z., & Jin, J. (2015). Clustered spatio-temporal compression design for wireless sensor networks. In 2015 24th international conference on computer communication and networks (ICCCN) (pp. 1–6). IEEE.Google Scholar
  27. 27.
    Quan, L., Xiao, S., Xue, X., & Lu, C. (2016). Neighbor-aided spatial-temporal compressive data gathering in wireless sensor networks. IEEE Communications Letters, 20(3), 578–581.CrossRefGoogle Scholar
  28. 28.
    Mahmudimanesh, M., Khelil, A., & Suri, N. (2012). Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks. In 2012 IEEE 9th international conference on mobile adhoc and sensor systems (MASS) (pp. 389–397). IEEE.Google Scholar
  29. 29.
    Zonoobi, D., & Kassim, A. A. (2012). Weighted-CS for reconstruction of highly under-sampled dynamic MRI sequences. In Signal & information processing association annual summit and conference (APSIPA ASC), 2012 Asia-Pacific (pp. 1–5). IEEE.Google Scholar
  30. 30.
    Chen, H., Ma, X., Zhang, Y., Tang, W. (2010). An iterative weighing algorithm for image reconstruction in compressive sensing. In 2010 first international conference on pervasive computing signal processing and applications (PCSPA) (pp. 1091–1094). IEEE.Google Scholar
  31. 31.
    Weizman, L., Eldar, Y. C., & Bashat, D. B. (2015). Compressed sensing for longitudinal MRI: An adaptive-weighted approach. Medical Physics, 42(9), 5195–5208.CrossRefGoogle Scholar
  32. 32.
    Zheng, H., Yang, F., Tian, X., Gan, X., Wang, X., & Xiao, S. (2015). Data gathering with compressive sensing in wireless sensor networks: A random walk based approach. IEEE Transactions on Parallel and Distributed Systems, 26(1), 35–44.CrossRefGoogle Scholar
  33. 33.
    Grant, M., & Boyd, S. (2014). CVX: Matlab software for disciplined convex programming.
  34. 34.
    National Center for Atmospheric Research Staff. (2013). The climate data guide: SST (AMSR-E): Sea surface temperature from remote sensing systems.
  35. 35.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (Vol. 2, p. 10). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, School of Electrical and Computer EngineeringShiraz UniversityShirazIran

Personalised recommendations