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Spatio-temporal modelling-based drift-aware wireless sensor networks

Spatio-temporal modelling-based drift-aware wireless sensor networks

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  • Author(s): M. Takruri 1 ; S. Rajasegarar 2 ; S. Challa 3 ; C. Leckie 4 ; M. Palaniswami 2
    • View affiliations
    • Affiliations: 1: Centre for Real-Time Information Networks (CRIN), University of Technology, Sydney, Australia
      2: Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
      3: NICTA Victoria Research Laboratory, University of Melbourne, Melbourne, Australia
      4: NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, Melbourne, Australia
  • Source: Volume 1, Issue 2, June 2011, p. 110 – 122
    DOI:  10.1049/iet-wss.2010.0091 , Print ISSN 2043-6386, Online ISSN 2043-6394
© The Institution of Engineering and Technology
Published

Wireless sensor networks are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiations of drifts in sensors are uncorrelated, the authors present a novel algorithm for detecting and correcting sensor measurement errors. The authors use statistical modelling rather than physical relations to model the spatio-temporal cross-correlations among sensors. This in principle makes the framework presented applicable to most sensing problems. Each sensor in the network trains a support vector regression algorithm on its neighbours' corrected readings to obtain a predicted value for its future measurements. This phase is referred to here as the training phase. In the running phase, the predicted measurements are used by each node, in a recursive decentralised fashion, to self-assess its measurement and to detect and correct its drift and random error using an unscented Kalman filter. No assumptions regarding the linearity of drift or the density (closeness) of sensor deployment are made. The authors also demonstrate using real data obtained from the Intel Berkeley Research Laboratory that the proposed algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.

Inspec keywords: sensor placement; spatiotemporal phenomena; Kalman filters; wireless sensor networks; statistical analysis; regression analysis; support vector machines

Other keywords: sensor deployment; statistical modelling; Kalman filter; support vector regression; spatiotemporal modelling; network lifetime; wireless sensor networks; measurement errors

Subjects: Wireless sensor networks; Filtering methods in signal processing; Other topics in statistics

References

    1. 1)
      • K.-R. Muller , A.J. Smola , G. Rätsch , B. Schökopf , J. Kohlmorgen , V. Vapnik . (1999) Using support vector machines for time series prediction.
    2. 2)
      • S. Boyd , L. Vandenberghe . (2004) Convex optimization.
    3. 3)
    4. 4)
      • Takruri, M., Challa, S.: `Drift aware wireless sensor networks', Proc. 10th Int. Conf. on Information Fusion, July 2007.
    5. 5)
    6. 6)
    7. 7)
      • Lu, S., Cai, L., Lu, D., Chen, J.: `Two efficient implementation forms of unscented Kalman filter', IEEE Int. Conf. on Control and Automation, 2007, p. 761–764.
    8. 8)
      • He, L.-M.: `Improved time synchronization in wireless sensor networks', 10thACIS Int. Conf. on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, 2009, SNPD’09, May 2009, p. 421–426.
    9. 9)
      • S. Challa , R. Evans , M. Morelande , D. Musicki . (2011) Fundamentals of object tracking.
    10. 10)
      • Estrin, D., Girod, L., Pottie, G., Srivastava, M.: `Instrumenting the world with wireless sensor networks', Int. Conf. on Acoustics, Speech, and Signal Processing, May 2001.
    11. 11)
      • Balzano, L.: `Addressing fault and calibration in wireless sensor networks', 2007, Master's, University of California, Los Angeles, California.
    12. 12)
    13. 13)
      • B. Sundararaman , U. Buy , A.D. Kshemkalyani . Clock synchronization for wireless sensor networks: a survey. Ad Hoc Netw. , 281 - 323
    14. 14)
      • Y.M. Wang , R.T. Schultz , R.T. Constable , .H. Staib1 L . Nonlinear estimation and modeling of FMRI data using spatio-temporal support vector regression. Inf. Process. Med. Imaging , 647 - 659
    15. 15)
      • D.G. Luenberger . (1989) Linear and nonlinear programming.
    16. 16)
      • Julier, S.: `The scaled unscented transformation', American Control Conf., vol 6, 2002, p. 4555–4559.
    17. 17)
      • Platt, J.: `Sequential minimal optimization: a fast algorithm for training support vector machines', Technical report 98-14, April 1998, Microsoft Research, Redmond, Washington.
    18. 18)
      • ‘http://db.lcs.mit.edu/labdata/labdata.html’ [online]. Accessed on 07/09/2006.
    19. 19)
      • B. Schölkopf , A. Smola . (2002) Learning with kernels.
    20. 20)
      • Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: `SVM and kernel methods MATLAB toolbox', Perception Systemes et Information, INSA de Rouen, 2005, Rouen, France.
    21. 21)
      • Bychkovskiy, V., Megerian, S., Estrin, D., Potkonjak, M.: `A collaborative approach to in-place sensor calibration', Int. Workshop on Information Processing in Sensor Networks, 2003, p. 301–316.
    22. 22)
      • D.M.J. Tax , R.P.W. Duin . Support vector data description. Mach. Learn. , 1 , 45 - 66
    23. 23)
      • Sommer, P., Wattenhofer, R.: `Gradient clock synchronization in wireless sensor networks', Proc. 2009 Int. Conf. on Information Processing in Sensor Networks, IPSN’09, 2009, Washington, DC, USA, p. 37–48.
    24. 24)
      • S. Lee , Y. Nah , L. Choi , S. Min , R. Pettit , P. Puschner , T. Ungerer . (2011) Reactive clock synchronization for wireless sensor networks with asynchronous wakeup scheduling, Software technologies for embedded and ubiquitous systems.
    25. 25)
      • R.E. Kalman . A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Engng. , 35 - 45
    26. 26)
      • Takruri, M., Aboura, K., Challa, S.: `Distributed recursive algorithm for auto calibration in drift aware wireless sensor networks', Int. Joint Conf. on Computer, Information, and Systems Sciences, and Engineering, December 2007.
    27. 27)
    28. 28)
      • Smola, A., Scholkopf, B., Ratsch, G.: `Linear programs for automatic accuracy control in regression', Proc. Int. Conf. on Artificial Neural Networks, 1999.
    29. 29)
    30. 30)
    31. 31)
      • S.M. Clarke , J.H. Griebsch , T.W. Simpson . Analysis of support vector regression for approximation of complex engineering analyses. J. Mech. Des. , 6 , 1077 - 1087
    32. 32)
      • Julier, S., Uhlmann, J.: `A new extension of the Kalman filter to nonlinear systems', Int. Symp. Aerospace/Defense Sensing, Simulation and Controls, 1997.
    33. 33)
      • S. Särkkä , J. Hartikainen . EKF/UKF toolbox for MATLAB v1.2.
    34. 34)
      • Takruri, M., Challa, S., Chakravorty, R.: `Auto calibration in drift aware wireless sensor networks using the interacting multiple model algorithm', Mosharaka Int. Conf. on Communications, Computers and Applications MIC-CCA 2008, August 2008.
    35. 35)
      • Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: `Online outlier detection in sensor data using non-parametric models', VLDB’06, 2006, p. 187–198.
    36. 36)
      • Okello, N., Pulford, G.: `Simultaneous registration and tracking for multiple radars with cluttered measurements', IEEE Signal Processing Workshop on Statistical Signal and Array Processing, June 1996, p. 60–63.
    37. 37)
      • O.L. Mangasarian , D.R. Musicant . Large scale kernel regression via linear programming. Mach. Learn. , 255 - 269
    38. 38)
    39. 39)
      • Bian, T., Venkatesan, R., Li, C.: `Adaptive time synchronization for wireless sensor networks with self-calibration', Proc. 2009 IEEE Int. Conf. on Communications, ICC’09, 2009, Piscataway, NJ, USA, p. 5031–5035.
    40. 40)
      • Okello, N., Challa, S.: `Simultaneous registration and track fusion for networked trackers', Conf. on Information Fusion, August 2003.
    41. 41)
      • M.K. Gill , M.W. Kemblowski , M. McKee . Soil moisture data assimilation using support vector machines and ensemble Kalman filter. J. Am. Water Resour. Assoc. , 4 , 1004 - 1015
    42. 42)
      • Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.F.: `A new approach for filtering nonlinear systems', American Control Conference, June 1995, p. 1628–1632.
    43. 43)
      • L. Balzano , R. Nowak . Blind calibration of sensor networks. Proc. Information Processing in Sensor Networks , 79 - 88
    44. 44)
      • Wan, E., van der Merwe, R.: `The unscented Kalman filter for nonlinear estimation', IEEE Symp. 2000 (AS-SPCC), October 2000.
    45. 45)
    46. 46)
      • Takruri, M., Rajasegarar, S., Challa, S., Leckie, C., Palaniswami, M.: `Online drift correction in wireless sensor networks using spatio-temporal modeling', Int. Conf. on Information Fusion, July 2008.
    47. 47)
      • M. Takruri , K. Aboura , S. Challa , K. Elleithy . (2008) Distributed recursive algorithm for auto calibration in drift aware wireless sensor networks, Innovations and advanced techniques in systems, computing sciences and software engineering.
    48. 48)
      • V.N. Vapnik . (1998) Statistical learning theory.
    49. 49)
    50. 50)
    51. 51)
      • Y. Bar-Shalom . (1993) Estimation and tracking: principle and software.
    52. 52)
    53. 53)
      • J. Feng , S. Megerian , M. Potkonjak . Model-based calibration for sensor networks. Sensors , 737 - 742
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