skip to main content
research-article

Adaptive calibration for fusion-based cyber-physical systems

Published:01 January 2013Publication History
Skip Abstract Section

Abstract

Many Cyber-Physical Systems (CPS) are composed of low-cost devices that are deeply integrated with physical environments. As a result, the performance of a CPS system is inevitably undermined by various physical uncertainties, which include stochastic noises, hardware biases, unpredictable environment changes, and dynamics of the physical process of interest. Traditional solutions to these issues (e.g., device calibration and collaborative signal processing) work in an open-loop fashion and hence often fail to adapt to the uncertainties after system deployment. In this article, we propose an adaptive system-level calibration approach for a class of CPS systems whose primary objective is to detect events or targets of interest. Through collaborative data fusion, our calibration approach features a feedback control loop that exploits system heterogeneity to mitigate the impact of aforementioned uncertainties on the system performance. In contrast to existing heuristic-based solutions, our control-theoretical calibration algorithm can ensure provable system stability and convergence. We also develop a routing algorithm for fusion-based multihop CPS systems that is robust to communication unreliability and delay. Our approach is evaluated by both experiments on a testbed of Tmotes as well as extensive simulations based on data traces gathered from a real vehicle detection experiment. The results demonstrate that our calibration algorithm enables a CPS system to maintain the optimal sensing performance in the presence of various system and environmental dynamics.

References

  1. Adbelzaher, T., Diao, Y., Hellerstein, J. L., Lu, C., and Zhu, X. 2008. Introduction to control theory and its application to computing systems. In Proceedings of the SIGMETRICS Conference on Performance Modeling and Engineering. Springer, 185--215.Google ScholarGoogle Scholar
  2. Ash, R. B. and Doleans-Dade, C. A. 1999. Probability and Measure Theory, 2nd Ed. Harcourt Science and Technology.Google ScholarGoogle Scholar
  3. Balzano, L. and Nowak, R. 2007. Blind calibration of sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'07). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bychkovsky, V., Megerian, S., Estrin, D., and Potkonjak, M. 2003. A collaborative approach to in-place sensor calibration. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'03). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chair, Z. and Varshney, P. 1986. Optimal data fusion in multiple sensor detection systems. IEEE Trans. Aerospace Electron. Syst. 22, 1, 98--101.Google ScholarGoogle ScholarCross RefCross Ref
  6. Chen, J., Tan, R., Xing, G., Wang, X., and Fu, X. 2010. Fidelity-Aware utilization control for cyber-physical surveillance systems. In Proceedings of the International IEEE Real-Time Systems Symposium (RTSS'10). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Clouqueur, T., Saluja, K. K., and Ramanathan, P. 2004. Fault tolerance in collaborative sensor networks for target detection. IEEE Trans. Comput. 53, 3, 320--333. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Duarte, M. and Hu, Y.-H. 2003. Distance based decision fusion in a distributed wireless sensor network. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'03). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Duarte, M. and Hu, Y.-H. 2004. Vehicle classification in distributed sensor networks. J. Parallel Distrib. Comput. 64, 7, 826--838. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Duda, R., Hart, P., and Stork, D. 2001. Pattern Classification. Wiley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Dutta, P., Arora, A., and Bibyk, S. 2006. Towards radar-enabled sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'06). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Dutta, P., Grimmer, M., Arora, A., Bibyk, S., and Culler, D. 2005. Design of a wireless sensor network platform for detecting rare, random and ephemeral events. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'05). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Feng, J., Megerian, S., and Potkonjak, M. 2003. Model-Based calibration for sensor networks. In Proceedings of the Conference on Sensors. IEEE.Google ScholarGoogle Scholar
  14. Girod, L., Lukac, M., Trifa, V., and Estrin, D. 2006. The design and implementation of a self-calibrating distributed acoustic sensing platform. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys'06). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Hata, M. 1980. Empirical formula for propagation loss in land mobile radio services. IEEE Trans. Vehic. Technol. 29, 3, 317--325.Google ScholarGoogle ScholarCross RefCross Ref
  16. He, T., Krishnamurthy, S., Stankovic, J. A., Abdelzaher, T., Luo, L., Stoleru, R., Yan, T., Gu, L., Hui, J., and Krogh, B. 2004. Energy-Efficient surveillance system using wireless sensor networks. In Proceedings of the International Conference on Mobile Systems, Applications and Services (MobiSys'04). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Hwang, J., He, T., and Kim, Y. 2007. Exploring in-situ sensing irregularity in wireless sensor networks. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys'07). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ihler, A., Fisher III, J., Moses, R., and Willsky, A. 2004. Nonparametric belief propagation for self-calibration in sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'04). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Kim, Y., Schmid, T., Charbiwala, Z. M., Friedman, J., and Srivastava, M. B. 2008. NAWMS: Nonintrusive autonomous water monitoring system. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys'08). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kim, Y., Schmid, T., Charbiwala, Z. M., and Srivastava, M. B. 2009. ViridiScope: Design and implementation of a fine grained power monitoring system for homes. In Proceedings of the International Conference on Ubiquitous Computing (UbiComp'09). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Le, H., Henriksson, D., and Abdelzaher, T. 2007. A control theory approach to throughput optimization in multi-channel collection sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN'07). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Li, D. and Hu, Y.-H. 2003. Energy based collaborative source localization using acoustic micro-sensor array. J. EUROSIP Appl. Signal Process. 4, 321--337. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Moteiv Corp. 2006. Tmote sky datasheet. http://www.moteiv.com/.Google ScholarGoogle Scholar
  24. NIST/SEMATECH. 2010. E-Handbook of Statistical Methods. National Institute of Standards and Technology. http://www.itl.nist.gov/div898/handbook/.Google ScholarGoogle Scholar
  25. Niu, R. and Varshney, P. K. 2005. Distributed detection and fusion in a large wireless sensor network of random size. J. EUROSIP Wirel. Comm. Netw. 4, 462--472. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ogata, K. 1995. Discrete-Time Control Systems. Prentice-Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Harmon, T., Harvey, C., Jay, J., Kohler, E., Rothenberg, S., and Srivastava, M. 2006. Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks. Tech. rep., Center for Embedded Networked Sensing.Google ScholarGoogle Scholar
  28. Sheng, X. and Hu, Y.-H. 2005. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Trans. Signal Process. 53, 1, 44--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Shi, L., Johansson, K., and Murray, R. 2007. Change sensor topology when needed: How to efficiently use system resources in control and estimation over wireless networks. In Proceedings of the IEEE Conference on Decision and Control (CDC'07).Google ScholarGoogle Scholar
  30. Tan, R., Xing, G., Liu, B., and Wang, J. 2009. Impact of data fusion on real-time detection in sensor networks. In Proceedings of the IEEE International Real-Time Systems Symposium (RTSS'09). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Tan, R., Xing, G., Wang, J., and So, H. C. 2008. Collaborative target detection in wireless sensor networks with reactive mobility. In Proceedings of the International Workshop on Quality of Service (IWQoS'08).Google ScholarGoogle Scholar
  32. Tan, R., Xing, G., Wang, J., and So, H. C. 2010. Exploiting reactive mobility for collaborative target detection in wireless sensor networks. IEEE Trans. Mobile Comput. (To appear). Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Varshney, P. K. 1996. Distributed Detection and Data Fusion. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Vigorito, C., Ganesan, D., and Barto, A. 2007. Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In Proceedings of the IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON'07).Google ScholarGoogle Scholar
  35. Whitehouse, K. and Culler, D. 2002. Calibration as parameter estimation in sensor networks. In Proceedings of the International Conference on Wireless Sensor Networks and Applications (WSNA'02). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Woo, A., Tong, T., and Culler, D. 2003. Taming the underlying challenges of reliable multihop routing in sensor networks. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys'03). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Wren, C., Erdem, U., and Azarbayejani, A. 2006. Functional calibration for pan-tilt-zoom cameras in hybrid sensor networks. Multimedia Syst. 12, 3, 255--268.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Xing, G., Tan, R., Liu, B., Wang, J., Jia, X., and Yi, C.-W. 2009. Data fusion improves the coverage of wireless sensor networks. In Proceedings of the ACM/IEEE Annual International Conference on Mobile Computing and Networking (MobiCom'09). ACM Press, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Zuniga, M. and Krishnamachari, B. 2004. Analyzing the transitional region in low power wireless links. In Proceedings of the IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON'04).Google ScholarGoogle Scholar

Index Terms

  1. Adaptive calibration for fusion-based cyber-physical systems

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!