DOI QR코드

DOI QR Code

Efficient Measurement Method for Spatiotemporal Compressive Data Gathering in Wireless Sensor Networks

  • Xue, Xiao (State Key Laboratory of Integrated Services Networks, Xidian University) ;
  • Xiao, Song (State Key Laboratory of Integrated Services Networks, Xidian University) ;
  • Quan, Lei (School of Aerospace Science and Technology, Xidian University)
  • 투고 : 2017.06.06
  • 심사 : 2017.11.02
  • 발행 : 2018.04.30

초록

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China, Central Universities

참고문헌

  1. Akyildiz I F, Su W, Sankarasubramaniam Y, et al., "Wireless sensor networks: a survey," Computer Networks, vol.38, no.4, pp. 393-422, March, 2002. https://doi.org/10.1016/S1389-1286(01)00302-4
  2. Mainwaring A., Polastre J.,Szewczyk R., et al.,"Wireless sensor networks for habitat monitoring," in Proc. of WSNA'02 pro. Of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp. 88-97, September 28-28, 2002.
  3. Z. Xiong, A. Liveris, and S. Cheng, "Distributed source coding for sensor networks", IEEE Signal Processing Magazine, vol. 21, no. 5, pp. 80-94, September, 2004. https://doi.org/10.1109/MSP.2004.1328091
  4. G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, "Energy conservation in wireless sensor networks: A survey," Ad Hoc Networks, vol. 7, no. 3, pp. 537-568, May, 2009. https://doi.org/10.1016/j.adhoc.2008.06.003
  5. D. L. Donoho, "Compressed sensing," IEEE Transactions Information Theory, vol. 52, no. 4, pp. 1289-1306, April, 2006. https://doi.org/10.1109/TIT.2006.871582
  6. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions Information Theory, vol. 52, no. 2, pp. 489-509, February, 2006. https://doi.org/10.1109/TIT.2005.862083
  7. E. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?," IEEE Transactions Information Theory, vol. 52, no. 12, pp. 5406-5425, December, 2006. https://doi.org/10.1109/TIT.2006.885507
  8. E. J. Candes and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, March, 2008. https://doi.org/10.1109/MSP.2007.914731
  9. Haupt J., Nowak R., "Signal reconstruction from noisy random projections," IEEE Transactions on Information Theory, vol. 52,no. 9, pp. 4036-4048, September, 2006. https://doi.org/10.1109/TIT.2006.880031
  10. Bajwa W U., Haupt J D., Sayeed A M, et al., "Joint source-channel communication for distributed estimation in sensor networks," IEEE Transactions on Information Theory, vol. 53, no. 10, pp. 3629-3653, October, 2007. https://doi.org/10.1109/TIT.2007.904835
  11. Luo C., Wu F., Sun J., et al., "Compressive data gathering for large-scale wireless sensor networks," in Proc. of 15th annual International Conf. on Mobile Computing and Networking, pp. 145-156, September 20-25, 2009.
  12. Luo C., Wu F., Sun J., et al., "Efficient measurement generation and pervasive sparsity for compressive data gathering," IEEE Transactions on Wireless Communications, vol. 9, no. 12, pp. 3728-3738, December, 2010. https://doi.org/10.1109/TWC.2010.092810.100063
  13. Wang J, Tang S, Yin B, et al., "Data gathering in wireless sensor networks through intelligent compressive sensing," in Proc. of IEEE INFOCOM, pp. 603-611, March 25-30, 2012.
  14. Wang W, Garofalakis M, Ramchandran K.W., "Distributed sparse random projections for refinable approximation," in Proc. of the 6th International conference on Information processing in sensor networks, pp. 331-339, April 25-27, 2007.
  15. Lee S., Pattem S., Sathiamoorthy M., et al., "Compressed sensing and routing in multi-hop networks," Usc Ceng Technical Report, 2010.
  16. M. Sartipi and R. Fletcher, "Energy-efficient data acquisition in wireless sensor networks using compressed sensing," in Proc. of the 2011 Data Compression Conference, pp. 223-232, March 29-31, 2011.
  17. Wu X, Xiong Y, Yang P, et al., "Sparsest Random Scheduling for Compressive Data Gathering in Wireless Sensor Networks," IEEE Transactions on Wireless Communications, vol. 13, no. 10, pp. 5867-5877, October, 2014. https://doi.org/10.1109/TWC.2014.2332344
  18. Xiang L, Luo J, Vasilakos A., "Compressed data aggregation for energy efficient wireless sensor networks," in Proc. 8th Annual IEEE Communication Society Conf. on Sensor, Mesh and Ad Hoc Communications and Networks, pp. 46-54, June, 27-30, 2011.
  19. Chen W, Wassell I J., "Optimized node selection for compressive sleeping wireless sensor networks," IEEE Transaction on Vehicular Technology, vol. 65, no. 2, pp. 827-836, February, 2015. https://doi.org/10.1109/TVT.2015.2400635
  20. Chen W, Wassell I J., "Compressive sleeping wireless sensor networks with active node selection," in Proc. of IEEE Global Communications Conference, pp. 7-12, December 8-12, 2014.
  21. Xiang L, Luo J, Rosenberg C., "Compressed data aggregation: Energy-efficient and high-fidelity data collection," IEEE/ACM Transactions on Networking., vol. 21, no. 6, pp. 1722-1735, December, 2013. https://doi.org/10.1109/TNET.2012.2229716
  22. Chou C T, Rana R, Hu W., "Energy efficient information collection in wireless sensor networks using adaptive compressive sensing," in Proc. of IEEE 34th Conf. on Local Computer Networks, pp. 443-450, October 20-23, 2009.
  23. G. Quer, R. Masiero, G. Pillonetto, et al., "Sensing, compression, recovery for WSNs: Sparse signal modeling and monitoring framework," IEEE Transactions on Wireless Communications, vol. 11, no. 10, pp. 3447-3461, October, 2012. https://doi.org/10.1109/TWC.2012.081612.110612
  24. M. Leinonen, M. Codreanu, and M. Juntti, "Sequential compressed sensing with progressive signal reconstruction in wireless sensor networks," IEEE Transactions on Wireless Communications, vol. 14, no. 3, pp.1622-1635, March, 2015. https://doi.org/10.1109/TWC.2014.2371017
  25. He J, Sun G, Li Z, et al.,"Compressive data gathering with low-rank constraints for Wireless Sensor networks," Signal Processing, vol. 131, no. C, pp. 73-76, February, 2017. https://doi.org/10.1016/j.sigpro.2016.08.002
  26. Gong B, Cheng P, Chen Z, et al., "Spatiotemporal compressive network coding for energy-efficient distributed data storage in wireless sensor networks," IEEE Communications Letters, vol. 19, no.5, pp. 803-806, May,2015. https://doi.org/10.1109/LCOMM.2015.2401008
  27. Wang C, Cheng P, Chen Z, et al., "Practical spatiotemporal compressive network coding for energy-efficient distributed data storage in wireless sensor networks," in Proc. of IEEE 81st. Vehicular Technology Conference, pp. 1-6, May 11-14, 2015.
  28. M. Leinonen, M. Codreanu, and M. Juntti, "Distributed correlated data gathering in wireless sensor networks via compressed sensing," in Proc. of Asilomar Conference on Signals, Systems and Computers, pp. 418-422, November 3-6, 2013.
  29. Y. Rivenson and A. Stern, "Compressed imaging with a separable sensing operator," IEEE Signal Processing Letters, vol. 16, no. 6, pp. 449-452, June, 2009. https://doi.org/10.1109/LSP.2009.2017817
  30. Duarte M F and Baraniuk R G., "Kronecker product matrices for compressive sensing," in Proc. of IEEE International Conference on Acoustics Speech and Signal Processing, pp. 3650-3653, March 14-19, 2010.
  31. Duarte M F and Baraniuk R G., " Kronecker Compressive Sensing," IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 494-504, February, 2012. https://doi.org/10.1109/TIP.2011.2165289
  32. E. Candes and J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Problems, vol. 23, no. 3, pp. 969-985, April, 2007. https://doi.org/10.1088/0266-5611/23/3/008
  33. Yang X, Tao X, Dutkiewicz E, et al., "Energy-Efficient Distributed Data Storage for Wireless Sensor Networks Based on Compressed Sensing and Network Coding," IEEE Transactions on Wireless Communications, vol. 12, no. 10, pp. 5087-5099, October, 2013. https://doi.org/10.1109/TWC.2013.090313.121804
  34. I. F. Akyildiz, M. C. Vuran, and O.B. Akan, "On exploiting spatial and temporal correlation in wireless sensor networks," in Proc. of WiOpt 2004: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, pp. 71-80, March 24-26, 2004.
  35. Luo C, Sun J, Wu F., "Compressive Network Coding for Approximate Sensor Data Gathering," in Proc. of IEEE Global Telecommunications Conference, pp. 1-6, December 5-9, 2011.
  36. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, "Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems," IEEE Journal of Selected Topics in Signal Processing, vol. 1, no. 4, pp. 586-597, December, 2007. https://doi.org/10.1109/JSTSP.2007.910281