Decentralized Gradient-based Field Motion Estimation with a Wireless Sensor Network
Daniel Fitzner, Monika Sester
2016
Abstract
Information on the advection of a spatio-temporal field is an important input to forecasting or interpolation algorithms. Examples include algorithms for precipitation interpolation or forecasting or the prediction of the evolution of dynamic oceanographic features advected by ocean currents. In this paper, an algorithm for the decentralized estimation of motion of a spatio-temporal field by the nodes of a stationary and synchronized Wireless Sensor Network (WSN) is presented. The approach builds on the well-known gradient-based optical flow method, which is extended to the specifics of WSNs and spatio-temporal fields, such as spatial irregularity of the samples, the strong constraints on computation and communication and the assumed motion constancy over sampling periods. A specification of the algorithm and a thorough analytical analysis of its communicational and computational complexity is provided. The performance of the algorithm is illustrated by simulations of a sensor network and a spatio-temporal moving field.
References
- Bowler, N. E. H., Pierce, C. E., Seed, A., 2004. Development of a precipitation nowcasting algorithm based upon optical flow techniques. Journal of Hydrology, vol. 288, no. 1-2, pp. 74-91.
- Brink, J., Pebesma, E., 2014. Plume Tracking with a Mobile Sensor Based on Incomplete and Imprecise Information. Transactions in GIS, vol. 18, no. 5, pp. 740-766.
- Das, J., Py, F., Maughan, T., O'Reilly, T., Messié, M., Ryan, J., Sukhatme, G.S., Rajan, K., 2012. Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters. The International Journal of Robotics Research, vol. 31, no. 5, pp. 626-646.
- Duckham, M., 2012. Decentralized Spatial Computing: Foundations of Geosensor Networks, Springer, Heidelberg.
- Fitzner, D., Sester, M., 2015. Estimation of precipitation fields from 1-minute rain gauge time series - comparison of spatial and spatio-temporal interpolation methods. International Journal of Geographical Information Science, vol. 29, nr. 9, pp. 1-26.
- Fitzner, D., Sester, M., Haberlandt, U., Rabiei, E., 2013. Rainfall Estimation with a Geosensor Network of Cars Theoretical Considerations and First Results. Photogrammetrie - Fernerkundung - Geoinformation, vol. 2013, no. 2, pp. 93-103.
- Fleet, D., Weiss, Y., 2006. Optical Flow Estimation, in: Paragios, N., Chen, Y., Faugeras, O. (Eds.), Handbook of Mathematical Models in Computer Vision. Springer US, pp. 237-257.
- Golub, G. H., Loan, C. F. V., 1996. Matrix Computations. JHU Press.
- Greg Welch, G. B., 2006. An Introduction to the Kalman Filter, University of North Carolina, Technical Report TR 95-041, July 24, 2006
- Haberlandt, U., Sester, M., 2010. Areal rainfall estimation using moving cars as rain gauges - a modelling study. Hydrol. Earth Syst. Sci., vol. 14, no. 7, pp. 1139-1151.
- Horn, B. K. P., Schunck, B. G., 1981. Determining optical flow. Artificial Intelligence, vol. 17, no. 1-3, pp. 185- 203.
- Huang, T. S., Hsu, Y. P., 1981. Image Sequence Enhancement, in: Huang, P.T.S. (Ed.), Image Sequence Analysis, Springer Series in Information Sciences. Springer Berlin Heidelberg, pp. 289-309.
- Jeong, M.-H., Duckham, M., Kealy, A., Miller, H. J., Peisker, A., 2014. Decentralized and coordinate-free computation of critical points and surface networks in a discretized scalar field. International Journal of Geographical Information Science, vol. 28, no. 1, pp. 1-21.
- Kalman, R., 1960. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME - Journal of Basic Engineering, vol. 82, no. 1, pp. 35- 45.
- Langley, R. B., 1999. Dilution of precision. GPS world, vol. 10, no. 5, 52-59.
- Lucas, B. D., Kanade, T., others, 1981. An iterative image registration technique with an application to stereo vision., Proceedings of the 7th Intl. Joint Conference on Artificial Intelligence, Vancouver, British Columbia, pp. 674-679.
- Särkkä, S., 2013. Bayesian Filtering and Smoothing. Cambridge University Press.
- Sester, M., 2009. Cooperative Boundary Detection in a Geosensor Network using a SOM, Proceedings of the International Cartographic Conference, Santiago, Chile.
- Tsai, H.-W., Chu, C.-P., Chen, T.-S., 2007. Mobile object tracking in wireless sensor networks. Computer Communications, vol. 30, no. 8, pp. 1811-1825.
- Umer, M., Kulik, L., Tanin, E., 2010. Spatial interpolation in wireless sensor networks: localized algorithms for variogram modeling and Kriging. Geoinformatica, vol. 14, no. 1, pp. 101-134.
- Watson, P. K., 1983. Kalman filtering as an alternative to Ordinary Least Squares - Some theoretical considerations and empirical results. Empirical Economics, vol. 8, no. 2, pp. 71-85.
- Zawadzki, I. I., 1973. Statistical Properties of Precipitation Patterns. Journal of Applied Meteorology, vol. 12, pp. 459-472.
Paper Citation
in Harvard Style
Fitzner D. and Sester M. (2016). Decentralized Gradient-based Field Motion Estimation with a Wireless Sensor Network . In Proceedings of the 5th International Confererence on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-169-4, pages 13-24. DOI: 10.5220/0005639100130024
in Bibtex Style
@conference{sensornets16,
author={Daniel Fitzner and Monika Sester},
title={Decentralized Gradient-based Field Motion Estimation with a Wireless Sensor Network},
booktitle={Proceedings of the 5th International Confererence on Sensor Networks - Volume 1: SENSORNETS,},
year={2016},
pages={13-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005639100130024},
isbn={978-989-758-169-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 5th International Confererence on Sensor Networks - Volume 1: SENSORNETS,
TI - Decentralized Gradient-based Field Motion Estimation with a Wireless Sensor Network
SN - 978-989-758-169-4
AU - Fitzner D.
AU - Sester M.
PY - 2016
SP - 13
EP - 24
DO - 10.5220/0005639100130024