Authors:
Daniel Fitzner
and
Monika Sester
Affiliation:
Leibniz University, Germany
Keyword(s):
Motion Estimation, Optical Flow, Wireless Sensor Network, Spatio-temporal Field.
Related
Ontology
Subjects/Areas/Topics:
Aggregation, Classification and Tracking
;
Applications and Uses
;
Data Manipulation
;
Environment Monitoring
;
Sensor Networks
;
Signal Processing
;
Statistical and Adaptive Signal Processing
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 networ
k and a spatio-temporal moving field.
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