Wireless sensor networks for long-term structural health monitoring

  • Meyer, Jonas (Empa, Swiss Federal Laboratories for Materials Testing and Research) ;
  • Bischoff, Reinhard (Empa, Swiss Federal Laboratories for Materials Testing and Research) ;
  • Feltrin, Glauco (Empa, Swiss Federal Laboratories for Materials Testing and Research) ;
  • Motavalli, Masoud (Empa, Swiss Federal Laboratories for Materials Testing and Research)
  • Received : 2008.05.01
  • Accepted : 2009.07.01
  • Published : 2010.04.25


In the last decade, wireless sensor networks have emerged as a promising technology that could accelerate progress in the field of structural monitoring. The main advantages of wireless sensor networks compared to conventional monitoring technologies are fast deployment, small interference with the surroundings, self-organization, flexibility and scalability. These features could enable mass application of monitoring systems, even on smaller structures. However, since wireless sensor network nodes are battery powered and data communication is the most energy consuming task, transferring all the acquired raw data through the network would dramatically limit system lifetime. Hence, data reduction has to be achieved at the node level in order to meet the system lifetime requirements of real life applications. The objective of this paper is to discuss some general aspects of data processing and management in monitoring systems based on wireless sensor networks, to present a prototype monitoring system for civil engineering structures, and to illustrate long-term field test results.



  1. Bennett, V., Abdoun, T., Shantz, T., Jang, D. and Thevanayagam, S. (2009), "Design and characterization of a compact array of MEMS accelerometers for geotechnical instrumentation", Smart Struct. Syst., 5(6), 663-679.
  2. Bischoff, R., Meyer, J., Feltrin, G. and Saukh, O. (2006), "Monitoring of civil infrastructures using wireless sensor networks", Proceedings of the APWSHM'06 1st Asia-Pacific Workshop on Structural Health Monitoring, Yokohama, December.
  3. Caffrey, J., Govindan, R., Johnson, E., Krishnamachari, B., Masri, S., Sukhatme, G., Chintalapudi, K., Dantu, K., Rangwala, S., Sridhara, A., Xu, N. and Zuniga, M. (2004), "Networked sensing for structural health monitoring", Proceedings of the 4th International Workshop on Structural Control, Ed. R. Betti, Columbia University, New York.
  4. Culler, D., Estrin, D. and Srivastava, M. (2004), "Overview of sensor networks", IEEE Comput., 37(8), 41-49.
  5. Feltrin, G., Meyer, J., Bischoff, R. and Saukh, O. (2006), "A wireless sensor network for force monitoring of cable stays", Proceedings of the 3rd International Conference on Bridge Maintenance, Safety and Management, IABMAS 06, Porto, July.
  6. Gsell, D. and Motavalli, M. (2004), "Indoor cable-stayed GFRP bridge at EMPA, Switzerland", Proceedings of the 4th International Conference on Advanced Composite Materials in Bridges and Structures, Calgary.
  7. Kim, S., Pakzad, S., Culler, D.E., Demmel, D., Fenves, D., Glaser, S. and Turon, M. (2006), Health monitoring of civil infrastructures using wireless sensor networks, Technical report No. UCB/EECS-2006-121, University of California, Berkeley, CA.
  8. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., Gay, D., Hill, J., Welsh, M., Brewer, E. and Culler, D. (2005), Tiny OS: An operating system for wireless sensor networks, Ambient Intelligence, Springer-Verlag, New York.
  9. Lu, K.C., Loh, C.H., Yang, Y.S., Lynch, J.P. and Law, K.H. (2008), "Real-time structural damage detection using wireless sensing and monitoring system", Smart Struct. Syst., 4(6), 759-777.
  10. Lynch, J.P., Yang, W., Loh, K.J., Yi, J.H. and Yun, C.B. (2006), "Performance monitoring of the Geumdang bridge using a dense network of high-resolution wireless sensors", Smart Mater. Struct., 15(6), 1561-1575.
  11. Lynch, J.P., Sundararajan, A., Law, K.H., Kiremidjian, A.S. and Carryer, E., (2003), "Powerefficient data management for a wireless structural monitoring system", Proceedings of the 4th International Workshop on Structural Health Monitoring, Ed. Chang, F.K., Stanford University, Stanford, CA.
  12. Mechitov, K., Kim, W., Agha, G. and Nagayama, T. (2006), "High-frequency distributed sensing for structure monitoring", Transac. Soc. Instr. Control Eng. (SICE), E-S-1(1), 109-114.
  13. Nagayama, T., Spencer, Jr., B.F., Mechitov, K.A. and Agha, G.A. (2009), "Middleware services for structural health monitoring using smart sensors", Smart Struct. Syst., 5(2), 119-137.
  14. Polastre, J., Szewczyk, R. and Culler, D. (2005), Telos: Enabling ultra-low power research, Information Processing in Sensor Networks/SPOTS, Berkeley, April.
  15. Sazonov, E., Janoyan, K. and Jha, R. (2004), "Wireless intelligent sensor network for autonomous structural health monitoring", Proceedings of SPIE's Annual International Symposium on Smart Structures and Materials, San Diego, CA.

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