Distributed data fusion algorithm for Wireless Sensor Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
PublisherIEEE Computer Society
Pages334-337
Number of pages4
ISBN (Print)9781479931064
DOIs
StatePublished - 2014
Event11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States
Duration: Apr 7 2014Apr 9 2014

Publication series

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

Conference

Conference11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
Country/TerritoryUnited States
CityMiami, FL
Period04/7/1404/9/14

Keywords

  • Digital Signal Processing
  • Distributed Kalman Filter
  • Wireless Sensor Network

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