Decentralized Gradient-based Field Motion Estimation with a Wireless Sensor Network

Daniel Fitzner, Monika Sester


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.


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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

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,},

in EndNote Style

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