Abstract

System size estimation in distributed wireless sensor networks is important in various applications such as network management and maintenance. One popular method for system size estimation is to use distributed consensus algorithms with randomly generated initial values at nodes. In this paper, the performance of such methods is studied and Fisher information and Cramer-Rao bounds (CRBs) for different consensus algorithms are derived. Errors caused by communication noise and lack of convergence is also considered, and their effect on Fisher information and CRB is given. The results provide a lower bound on the variance of the estimator of system size. This in turn, provides guidelines on how to choose consensus algorithms and initial values at the nodes.

Original languageEnglish (US)
Title of host publication2016 Sensor Signal Processing for Defence, SSPD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509003266
DOIs
StatePublished - Oct 13 2016
Event6th Conference of the Sensor Signal Processing for Defence, SSPD 2016 - Edinburgh, United Kingdom
Duration: Sep 22 2016Sep 23 2016

Other

Other6th Conference of the Sensor Signal Processing for Defence, SSPD 2016
CountryUnited Kingdom
CityEdinburgh
Period9/22/169/23/16

Fingerprint

Cramer-Rao bounds
Fisher information
Network management
Parallel algorithms
Wireless sensor networks
Computer systems
estimators
maintenance
Communication
communication
sensors

Keywords

  • Average Consensus
  • Communication Noise
  • Cramer-Rao Bound
  • Fisher Information
  • Max Consensus
  • System Size Estimation
  • Wireless Sensor Networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics
  • Instrumentation
  • Artificial Intelligence

Cite this

Zhang, S., Tepedelenlioglu, C., Lee, J., Braun, H., & Spanias, A. (2016). Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms. In 2016 Sensor Signal Processing for Defence, SSPD 2016 [7590591] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSPD.2016.7590591

Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms. / Zhang, Sai; Tepedelenlioglu, Cihan; Lee, Jongmin; Braun, Henry; Spanias, Andreas.

2016 Sensor Signal Processing for Defence, SSPD 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7590591.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, S, Tepedelenlioglu, C, Lee, J, Braun, H & Spanias, A 2016, Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms. in 2016 Sensor Signal Processing for Defence, SSPD 2016., 7590591, Institute of Electrical and Electronics Engineers Inc., 6th Conference of the Sensor Signal Processing for Defence, SSPD 2016, Edinburgh, United Kingdom, 9/22/16. https://doi.org/10.1109/SSPD.2016.7590591
Zhang S, Tepedelenlioglu C, Lee J, Braun H, Spanias A. Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms. In 2016 Sensor Signal Processing for Defence, SSPD 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7590591 https://doi.org/10.1109/SSPD.2016.7590591
Zhang, Sai ; Tepedelenlioglu, Cihan ; Lee, Jongmin ; Braun, Henry ; Spanias, Andreas. / Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms. 2016 Sensor Signal Processing for Defence, SSPD 2016. Institute of Electrical and Electronics Engineers Inc., 2016.
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abstract = "System size estimation in distributed wireless sensor networks is important in various applications such as network management and maintenance. One popular method for system size estimation is to use distributed consensus algorithms with randomly generated initial values at nodes. In this paper, the performance of such methods is studied and Fisher information and Cramer-Rao bounds (CRBs) for different consensus algorithms are derived. Errors caused by communication noise and lack of convergence is also considered, and their effect on Fisher information and CRB is given. The results provide a lower bound on the variance of the estimator of system size. This in turn, provides guidelines on how to choose consensus algorithms and initial values at the nodes.",
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