Distributed data fusion algorithm for Wireless Sensor Network

A. Abdelgawad

doi:10.1109/ICNSC.2014.6819648

Distributed data fusion algorithm for Wireless Sensor Network

A. Abdelgawad

Engineering & Technology, School Of

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Signal processing in Wireless Sensor Network (WSN) has a huge range of applications. Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. DKF finds applications in object tracking, environmental monitoring, surveillance, and many other applications. All algorithms proposed in the literature are based on static network. In reality, the network topology is changing. The topology change is often caused by node failure, which is due to energy depletion. In this work a DKF is proposed for such network. The simulation and the experimental results validate our proposed DKF. The experimental results show that each sensor node can run DKF with up to six neighbors.

Original language	English
Title of host publication	Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
Publisher	IEEE Computer Society
Pages	334-337
Number of pages	4
ISBN (Print)	9781479931064
DOIs	https://doi.org/10.1109/ICNSC.2014.6819648
State	Published - 2014
Event	11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States Duration: Apr 7 2014 → Apr 9 2014

Publication series

Name	Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

Conference

Conference	11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
Country/Territory	United States
City	Miami, FL
Period	04/7/14 → 04/9/14

Keywords

Digital Signal Processing
Distributed Kalman Filter
Wireless Sensor Network

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10.1109/ICNSC.2014.6819648

Cite this

Abdelgawad, A. (2014). Distributed data fusion algorithm for Wireless Sensor Network. In Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 (pp. 334-337). [6819648] (Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014). IEEE Computer Society. https://doi.org/10.1109/ICNSC.2014.6819648

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title = "Distributed data fusion algorithm for Wireless Sensor Network",

abstract = "Signal processing in Wireless Sensor Network (WSN) has a huge range of applications. Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. DKF finds applications in object tracking, environmental monitoring, surveillance, and many other applications. All algorithms proposed in the literature are based on static network. In reality, the network topology is changing. The topology change is often caused by node failure, which is due to energy depletion. In this work a DKF is proposed for such network. The simulation and the experimental results validate our proposed DKF. The experimental results show that each sensor node can run DKF with up to six neighbors.",

keywords = "Digital Signal Processing, Distributed Kalman Filter, Wireless Sensor Network",

author = "A. Abdelgawad",

year = "2014",

doi = "10.1109/ICNSC.2014.6819648",

language = "English",

isbn = "9781479931064",

series = "Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014",

publisher = "IEEE Computer Society",

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booktitle = "Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014",

note = "null ; Conference date: 07-04-2014 Through 09-04-2014",

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Abdelgawad, A 2014, Distributed data fusion algorithm for Wireless Sensor Network. in Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014., 6819648, Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014, IEEE Computer Society, pp. 334-337, 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014, Miami, FL, United States, 04/7/14. https://doi.org/10.1109/ICNSC.2014.6819648

Distributed data fusion algorithm for Wireless Sensor Network. / Abdelgawad, A.

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014. IEEE Computer Society, 2014. p. 334-337 6819648 (Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distributed data fusion algorithm for Wireless Sensor Network

AU - Abdelgawad, A.

PY - 2014

Y1 - 2014

N2 - Signal processing in Wireless Sensor Network (WSN) has a huge range of applications. Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. DKF finds applications in object tracking, environmental monitoring, surveillance, and many other applications. All algorithms proposed in the literature are based on static network. In reality, the network topology is changing. The topology change is often caused by node failure, which is due to energy depletion. In this work a DKF is proposed for such network. The simulation and the experimental results validate our proposed DKF. The experimental results show that each sensor node can run DKF with up to six neighbors.

AB - Signal processing in Wireless Sensor Network (WSN) has a huge range of applications. Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. DKF finds applications in object tracking, environmental monitoring, surveillance, and many other applications. All algorithms proposed in the literature are based on static network. In reality, the network topology is changing. The topology change is often caused by node failure, which is due to energy depletion. In this work a DKF is proposed for such network. The simulation and the experimental results validate our proposed DKF. The experimental results show that each sensor node can run DKF with up to six neighbors.

KW - Digital Signal Processing

KW - Distributed Kalman Filter

KW - Wireless Sensor Network

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U2 - 10.1109/ICNSC.2014.6819648

DO - 10.1109/ICNSC.2014.6819648

M3 - Conference contribution

AN - SCOPUS:84902384893

SN - 9781479931064

T3 - Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

SP - 334

EP - 337

BT - Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

PB - IEEE Computer Society

Y2 - 7 April 2014 through 9 April 2014

ER -

Distributed data fusion algorithm for Wireless Sensor Network

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