Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | SENSORNETS 2016 - Proceedings of the 5th International Confererence on Sensor Networks |
| Herausgeber/-innen | Andreas Ahrens, Octavian Postolache, Cesar Benavente-Peces |
| Seiten | 13-24 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 9789897581694 |
| Publikationsstatus | Veröffentlicht - 2016 |
| Veranstaltung | 5th International Confererence on Sensor Networks, SENSORNETS 2016 - Rome, Italien Dauer: 19 Feb. 2016 → 21 Feb. 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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Information systems
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- BibTex
- RIS
SENSORNETS 2016 - Proceedings of the 5th International Confererence on Sensor Networks. Hrsg. / Andreas Ahrens; Octavian Postolache; Cesar Benavente-Peces. 2016. S. 13-24.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Decentralized gradient-based field motion estimation with a wireless sensor network
AU - Fitzner, Daniel
AU - Sester, Monika
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Motion estimation
KW - Optical flow
KW - Spatio-temporal field
KW - Wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=84971469711&partnerID=8YFLogxK
U2 - 10.5220/0005639100130024
DO - 10.5220/0005639100130024
M3 - Conference contribution
AN - SCOPUS:84971469711
SP - 13
EP - 24
BT - SENSORNETS 2016 - Proceedings of the 5th International Confererence on Sensor Networks
A2 - Ahrens, Andreas
A2 - Postolache, Octavian
A2 - Benavente-Peces, Cesar
T2 - 5th International Confererence on Sensor Networks, SENSORNETS 2016
Y2 - 19 February 2016 through 21 February 2016
ER -