Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Distributed Decision-Making in Wireless Sensor Networks for Online Structural Health Monitoring

  • Ling, Qing (Department of Electrical and Computer Engineering, Michigan Technological University) ;
  • Tian, Zhi (Department of Electrical and Computer Engineering, Michigan Technological University) ;
  • Li, Yue (Department of Civil and Environmental Engineering, Michigan Technological University)
  • Published : 2009.08.31
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Abstract

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

Keywords

References

  1. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Commun. Mag. , vol. 40, pp. 102-114, 2002
  2. J. Kotecha, V. Ramachandran, and A. Sayeed, "Distributed multitarget classification in wireless sensor networks," IEEE J. Sel. Areas Commun., vol. 23, pp. 703-713, 2005
  3. J. Xiao, A. Ribeiro, Z. Luo, and G. Giannakis, "Distributed compression estimation using wireless sensor networks," IEEE Signal Process. Mag., vol. 23, pp. 27-41, 2006 https://doi.org/10.1109/MSP.2006.1657815
  4. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah'Randomized gossip al gorithms," IEEE Trans. Inf Theory, vol. 52, pp. 2508-2530, 2006 https://doi.org/10.1109/TIT.2006.874516
  5. J. Predd, S. Kulkami, and H. Poor, "Distributed leaming in wireless sensor networks," IEEE Signal Process. Mag. , vol. 23, pp. 56-69, 2006 https://doi.org/10.1109/MSP.2006.1657817
  6. C. Farrar, T. Duffey, S. Doebling, and D. Nix, "A statistical pattem recognition paradigm for vibriation-based structrural health monitoring," in Proc.lnt. Workshop on Structural Health Monitoring, 1999, pp. 1-10
  7. S. Doebling, C. Farrar, and M. Prime, 'A summ따y review of vibrationbased damage identifaction methods," The Shock and Vibration Digest, vol. 30, pp. 91-105, 1998 https://doi.org/10.1177/058310249803000201
  8. Y. Gao, B. Spencer, and M. Ruiz-Sandoval, 'Distributεd computing strat egy for structural hea1th monitoring," Structural Control and Health Monitoring, vol. 13, pp. 488-507, 2006 https://doi.org/10.1002/stc.117
  9. J. Lynch, "Embedding damage detection algorithms in a wireless sensing unit for operational power efficiency, "Smart Materials and Structures, vol. 13, pp. 800-810, 2004 https://doi.org/10.1088/0964-1726/13/4/018
  10. H. Sohn and C. Farrar, "Damage diagnosis using time series analysis of vibration signals," Smart Materials and Structures, vol. 10, pp. 445-451, 2001
  11. D. Donoho, "Comprcssed sensing," IEEE Trans. lnf. Theory, vol. 52, pp. 1289-1306, 2006 https://doi.org/10.1109/TIT.2006.871582
  12. F. Fagnani and S. Zampieri, " Randomized consensus algorithms over large scale networks," IEEE J. Sel. Areas Commun., vol.26, pp. 634-649, 2008 https://doi.org/10.1109/JSAC.2008.080506
  13. M. Rabbat and R. Nowak, "Distributed optimizatíon in sensor networks," in Proc. Int. Symposium on lnformation Processing in Sensor Networks, 2004, pp. 20-27
  14. I. Schizas, A. Ribeiro, and G. Giannakis, "Consensus in ad hoc WSNs with noisy links. Part I: distributed estimation of deterministc signal" IEEE Trans. Signal Process., vol. 56, pp. 350-364, 2008 https://doi.org/10.1109/TSP.2007.906734
  15. D. Bertsekas and J. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, 2nd Ed., Athena Scientific, 1997
  16. D. Bertsekas, Numerical Optimization, 2nd Ed., Athena Scientific, 1999
  17. J. Lynch, "An overview of wireless structural health monitoring for civil strucutres," Philosophical Tansactions of the Royal Society A, vol. 365, pp.345-372, 2007 https://doi.org/10.1098/rsta.2006.1932
  18. Q. Ling, Z. Tiall, Y. Yill, and Y Li, "Localized structural health monilorillg in wireless sensor networks:' IEEE Sensors J. , to appear
  19. P. Tsellg, "Distributed computation for linear programming problems satisfiying a certain diagonal dominance condition," Mathematics of Operations Research, vol. 15, pp. 33-48, 1990 https://doi.org/10.1287/moor.15.1.33
  20. Pacific Earthquake Engineering Research Centcr, "OpenSees: Open system for earthquake engineering simulation," 1999