Estimation over wireless sensor networks

Bonnie Zhu; Bruno Sinopoli; Kameshwar Poolla; Shankar Sastry

doi:10.1109/ACC.2007.4283115

Estimation over wireless sensor networks

Bonnie Zhu
, Bruno Sinopoli
, Kameshwar Poolla
, Shankar Sastry

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

25 Scopus citations

Abstract

Remote estimation problems are critical to many novel applications enabled by large-scale dense wireless sensor network. Individual sensors simultaneously sense, process and transmit measured information over a lossy wireless network to a central base station, which processes the data and produces an optimal estimate of the state. In this paper, we investigate the tradeoff between the estimation performance and the number of communicating nodes with respect to the major MAC protocols used in wireless sensor networks. We first construct a Markov model of the node behavior to study the correlation between packet reception probability and the number of communicating nodes. We then develop a multi-sensor measurement fusion model. This is used to feed a multi-sensor Kalman filtering algorithm to assess the impact of MAC protocols on estimation performance. We offer a target tracking example to illustrate our approach.

Original language	English
Title of host publication	Proceedings of the 2007 American Control Conference, ACC
Pages	2732-2737
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2007.4283115
State	Published - 2007
Event	2007 American Control Conference, ACC - New York, NY, United States Duration: Jul 9 2007 → Jul 13 2007

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Conference

Conference	2007 American Control Conference, ACC
Country/Territory	United States
City	New York, NY
Period	07/9/07 → 07/13/07

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10.1109/ACC.2007.4283115

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title = "Estimation over wireless sensor networks",

abstract = "Remote estimation problems are critical to many novel applications enabled by large-scale dense wireless sensor network. Individual sensors simultaneously sense, process and transmit measured information over a lossy wireless network to a central base station, which processes the data and produces an optimal estimate of the state. In this paper, we investigate the tradeoff between the estimation performance and the number of communicating nodes with respect to the major MAC protocols used in wireless sensor networks. We first construct a Markov model of the node behavior to study the correlation between packet reception probability and the number of communicating nodes. We then develop a multi-sensor measurement fusion model. This is used to feed a multi-sensor Kalman filtering algorithm to assess the impact of MAC protocols on estimation performance. We offer a target tracking example to illustrate our approach.",

author = "Bonnie Zhu and Bruno Sinopoli and Kameshwar Poolla and Shankar Sastry",

year = "2007",

doi = "10.1109/ACC.2007.4283115",

language = "English",

isbn = "1424409888",

series = "Proceedings of the American Control Conference",

pages = "2732--2737",

booktitle = "Proceedings of the 2007 American Control Conference, ACC",

note = "2007 American Control Conference, ACC ; Conference date: 09-07-2007 Through 13-07-2007",

}

TY - GEN

T1 - Estimation over wireless sensor networks

AU - Zhu, Bonnie

AU - Sinopoli, Bruno

AU - Poolla, Kameshwar

AU - Sastry, Shankar

PY - 2007

Y1 - 2007

N2 - Remote estimation problems are critical to many novel applications enabled by large-scale dense wireless sensor network. Individual sensors simultaneously sense, process and transmit measured information over a lossy wireless network to a central base station, which processes the data and produces an optimal estimate of the state. In this paper, we investigate the tradeoff between the estimation performance and the number of communicating nodes with respect to the major MAC protocols used in wireless sensor networks. We first construct a Markov model of the node behavior to study the correlation between packet reception probability and the number of communicating nodes. We then develop a multi-sensor measurement fusion model. This is used to feed a multi-sensor Kalman filtering algorithm to assess the impact of MAC protocols on estimation performance. We offer a target tracking example to illustrate our approach.

AB - Remote estimation problems are critical to many novel applications enabled by large-scale dense wireless sensor network. Individual sensors simultaneously sense, process and transmit measured information over a lossy wireless network to a central base station, which processes the data and produces an optimal estimate of the state. In this paper, we investigate the tradeoff between the estimation performance and the number of communicating nodes with respect to the major MAC protocols used in wireless sensor networks. We first construct a Markov model of the node behavior to study the correlation between packet reception probability and the number of communicating nodes. We then develop a multi-sensor measurement fusion model. This is used to feed a multi-sensor Kalman filtering algorithm to assess the impact of MAC protocols on estimation performance. We offer a target tracking example to illustrate our approach.

UR - https://www.scopus.com/pages/publications/46449093682

U2 - 10.1109/ACC.2007.4283115

DO - 10.1109/ACC.2007.4283115

M3 - Conference contribution

AN - SCOPUS:46449093682

SN - 1424409888

SN - 9781424409884

T3 - Proceedings of the American Control Conference

SP - 2732

EP - 2737

BT - Proceedings of the 2007 American Control Conference, ACC

T2 - 2007 American Control Conference, ACC

Y2 - 9 July 2007 through 13 July 2007

ER -

Estimation over wireless sensor networks

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