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Wireless Networks

, Volume 25, Issue 2, pp 675–687 | Cite as

Data ferries based compressive data gathering for wireless sensor networks

  • Siwang ZhouEmail author
  • Qian Zhong
  • Bo Ou
  • Yonghe Liu
Article
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Abstract

The latest research progress of the theory of compressive sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data for wireless sensor networks. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our learning strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of the gathered data. Simulation results show that the proposed scheme improves the reconstruction accuracy and stability compared to the existing ones.

Keywords

Data ferry Sensor network Compressive sensing Data gathering 
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Notes

Acknowledgements

Funding was provided by CERNET Innovation Project (NGII20160323).

References

  1. 1.
    Akyildiz, I. F., Su, Y. S. W., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communication Magazine, 40(8), 102–114.CrossRefGoogle Scholar
  2. 2.
    Culler, D., Estrin, D., & Srivastava, M. (2004). Overview of sensor networks. IEEE Computer Society, 37(8), 41–49.CrossRefGoogle Scholar
  3. 3.
    Rajagopalan, R., & Varshney, P. K. (2006). Data aggregation techniques in sensor networks: A survey. IEEE Communications Surveys and Tutorials, 8(4), 48–63.CrossRefGoogle Scholar
  4. 4.
    Zhang, H., & Shen, H. (2009). Balancing energy consumption to maximize network lifetime in data-gathering sensor networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1526–1539.CrossRefGoogle Scholar
  5. 5.
    Jiang, H., Jin, S., & Wang, C. (2011). Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(6), 1064–1071.CrossRefGoogle Scholar
  6. 6.
    Tang, X., & Xu, J. (2008). Optimizing lifetime for continuous data aggregation with precision guarantees in wireless sensor networks. IEEE Transactions on networking, 16(4), 904–917.CrossRefGoogle Scholar
  7. 7.
    Cands, E., & Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transaction on Information Theory, 52(12), 5406–5425.MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Donoho, D. L. (2006). Compressed sensing. IEEE Transaction on Information Theory, 52(4), 1289–1306.MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Xu, X., Ansari, R., Khorkhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing(HDACS). ACM Transaction on Sensor Networks, 11(3), Article 45.Google Scholar
  10. 10.
    Xu, X., Ansari, R., & Khorkhar, A. (2014). Adaptive hierarchical data aggregation using compressive sensing (A-HDACS) for non-smooth data field. In Proceedings of IEEE ICC.Google Scholar
  11. 11.
    Fazel, F., Fazel, M., & Stojanovic, M. (2011). Random access compressed sensing for energy-efficient underwater sensor networks. IEEE Journal on Selected Areas in Communications, 29(8), 1660–1670.CrossRefGoogle Scholar
  12. 12.
    Wang, J., Tang, S., Yin, B., & Li, X.-Y. (2012). Data gathering in wireless sensor networks through intelligent compressive sensing. In Proceedings of IEEE INFOCOM.Google Scholar
  13. 13.
    Zheng, H., Xiao, S., Wang, X., & Tian, X. (2012). Energy and latency analysis for in-network computation with compressive sensing in wireless sensor networks. In Proceedings of IEEE INFOCOM.Google Scholar
  14. 14.
    Sartipi, M., & Fletcher, R. (2011). Energy-efficient data acquisition in wireless sensor networks using compressed sensing. In Proceedings of IEEE Data Compression Conference (DCC) (pp. 223–232).Google Scholar
  15. 15.
    Sugihara, R., & Gupta, R. K. (2010). Speed control and scheduling of data mules in sensor networks. ACM Transactions on Sensor Networks, 7(1), Article 4.Google Scholar
  16. 16.
    Sugihara, R., & Gupta, R. K. (2010). Path planning of data mules in sensor networks. ACM Transactions on Sensor Networks, 8(1), Article 1.Google Scholar
  17. 17.
    He, L., Pan, J., & Xu, J. (2013). Progressive approach to reducing data collection latency in wireless sensor networks with mobile elements. IEEE Transactions on Mobile Computing, 12(7), 1308–1320.CrossRefGoogle Scholar
  18. 18.
    Chen, L., Wang, W., Huang, H., & Lin, S. (2015). Time-constrained data harvesting in WSNs: Theoretical foundation and algorithm design. In Proceedings of IEEE INFOCOM.Google Scholar
  19. 19.
    Qi, H., Xu, Y., & Wang, X. (2003). Mobile agent based collaborative signal and information processing in sensor networks. Proceedings of the IEEE, 91(8), 1172–1183.CrossRefGoogle Scholar
  20. 20.
    Wang, M., Xu, W., Mallada, E., & Tang, A. (2015). Sparse recovery with graph constraints. IEEE Transactions on Information Theory, 61(2), 1028–1044.MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Wang, M., Xu, W., Mallada, E., & Tang, A. (2012). Sparse recovery with graph constraints: Fundamental limits and measurement construction. In Proceedings of IEEE INFOCOM (pp. 1871–1879).Google Scholar
  22. 22.
    Xu, W., Mallada, E., & Tang, A. (2011). Compressive sensing over graphs. In Proceedings of IEEE INFOCOM (pp. 2087–2095).Google Scholar
  23. 23.
    Brabham, D. C. (2008). Crowdsourcing as a model for problem solving: An introduction and cases. Convergence: The International Journal of Research into New Media Technologies, 14(1), 75C90.Google Scholar
  24. 24.
    Luo, C., Wu, F. Sun, J., & Chen, C. W. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of ACM MobiCom (pp. 145–156).Google Scholar
  25. 25.
    Luo, C., Wu, F., Sun, J., & Chen, C. W. (2010). Efficient measurement generation and pervasive sparsity for compressive data gathering. IEEE Transaction on Wireless Communication, 9(12), 3728–3738.CrossRefGoogle Scholar
  26. 26.
    Luo, J., Xiang, L., & Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks? In Proceedings of the IEEE ICC, no. 1–6. Cape Town, South Africa.Google Scholar
  27. 27.
    Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In Proceedings of IEEE SECON.Google Scholar
  28. 28.
    Malioutov, D. M., Sanghavi, S. R., & Willsky, A. S. (2010). Sequential compressed sensing. IEEE Journal of Selected Topics in Signal Processing, 4(2), 435–444.CrossRefGoogle Scholar
  29. 29.
    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.CrossRefGoogle Scholar
  30. 30.
    Figueiredo, M., Nowak, R., & Wright, S. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing, 1(4), 586C598.CrossRefGoogle Scholar
  31. 31.
    Kim, S. J., Koh, K., Lustig, M., Boyd, S., & Gorinevsky, D. (2007). A method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing, 1(6), 606–617.CrossRefGoogle Scholar
  32. 32.
    Daubechies, I., De Friese, M., & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 57, 1413–1457.MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.MathSciNetCrossRefGoogle Scholar
  34. 34.
    Chen, S., Donoho, D. L., & Saunders, M. A. (1999). Atomic Decomposition by basis pursuit. Siam Journal on Scientific computing, 20(l), 33–61.MathSciNetzbMATHGoogle Scholar
  35. 35.
    National Oceanic and Atmospheric Administration is National Data Buoy Center. http://tao.ndbc.noaa.gov/refreshed/ctd_delivery.php.
  36. 36.
    Liu, Y., He, Y., Li, M., et al. (2013). Does wireless sensor network scale? A measurement study on GreenOrbs. IEEE Transactions on Parallel and Distributed Systems, 24(10), 1983–1993.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer Science and Electrical EngineeringHunan UniversityChangshaChina
  2. 2.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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