Abstract
In wireless sensor network applications, sensor measurements are corrupted by noises resulting from harsh environmental conditions, hardware and transmission errors. Minimising the impact of noise in an energy constrained sensor network is a challenging task. We study the problem of estimating environmental phenomena (e.g., temperature, humidity, pressure) based on noisy sensor measurements to minimise the estimation error. An environmental phenomenon is modeled using linear Gaussian dynamics and the Kalman filtering technique is used for the estimation. At each time step, a group of sensors is scheduled to transmit data to the base station to minimise the total estimated error for a given energy budget. The sensor scheduling problem is solved by dynamic programming and one-step-look-ahead methods. Simulation results are presented to evaluate the performance of both methods. The dynamic programming method produced better results with higher computational cost than the one-step-look-ahead method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nakamura, E.F., Loureiro, A.A.F., Frery, A.C.: Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Comput. Surv. 39(3), 9 (2007)
Yee Lin, T., Sehgal, V., Hamid, H.S.: Sensoclean: Handling noisy and incomplete data in sensor networks using modeling. Technical report, University of maryland (2005)
Takruri, M., Rajasegarar, S., Challa, S., Leckie, C., Palaniswami, M.: Online drift correction in wireless sensor networks using spatio-temporal modeling. In: 2008 11th International Conference on Information Fusion, pp. 1–8 (30 2008-July 3 2008)
Elnahrawy, E., Nath, B.: Cleaning and querying noisy sensors. In: WSNA 2003: Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications, pp. 78–87. ACM, New York (2003)
Bychkovskiy, V., Megerian, S., Estrin, D., Potkonjak, M.: A collaborative approach to in-place sensor calibration. In: Proceedings of the Second International Workshop on Information Processing in Sensor Networks IPSN, pp. 301–316 (2003)
McCulloch, J., Guru, S.M., McCarthy, P., Hugo, D., Peng, W., Terhorst, A.: Wireless sensor network deployment for water use efficiency in irrigation. In: REALWSN 2008: Proceedings of the workshop on Real-world wireless sensor networks, pp. 46–50. ACM, New York (2008)
Benth, J.S., Benth, F.E., Jalinskas, P.: A spatial-temporal model for temperature with seasonal variance. Journal of Applied Statistics 34(7), 823–841 (2007)
Data: Intel Lab Dataset, http://db.csail.mit.edu/labdata/labdata.html/
Maheswararajah, M., Halgamuge, S., Premaratne, M.: Sensor scheduling for target tracking by sub-optimal algorithms. IEEE Transactions on Vehicular Technology (2009) (accepted for publication)
Evans, J., Krishnamurthy, V.: Optimal sensor scheduling for hidden markov models. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP 1998, vol. 4, pp. 2161–2164 (1998)
Kalman, R.E.: A new approach to linear filtering and prediction problem. Transaction of ASME, Journal of Basic Engineering on Automatic Control, 35–45 (March 1960)
Song, E., Zhu, Y., Zhou, J.: The optimality of kalman filtering fusion with cross-correlated sensor noises. In: 43rd IEEE Conference on Decision and Control, 2004. CDC, vol. 5 (December 2004)
Bertsekas, D.P.: Dynamic Programming and Optimal Control, 3rd edn., vol. 1. Athena Scientific (2005)
Williams, J.L., Fisher, J.W., Willsky, A.S.: Approximate dynamic programming for communication-constrained sensor network management. IEEE Transactions on signal Processing 55(8), 4300–4311
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maheswararajah, S., Guru, S.M., Shu, Y., Halgamuge, S. (2009). Energy Adaptive Sensor Scheduling for Noisy Sensor Measurements. In: Krishnamachari, B., Suri, S., Heinzelman, W., Mitra, U. (eds) Distributed Computing in Sensor Systems. DCOSS 2009. Lecture Notes in Computer Science, vol 5516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02085-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-02085-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02084-1
Online ISBN: 978-3-642-02085-8
eBook Packages: Computer ScienceComputer Science (R0)