Sensor selection and routing design for state estimation over wireless sensor networks

Yuchi Wu; Takuya Iwaki; Junfeng Wu; Karl Henrik Johansson; Ling Shi

doi:10.23919/ChiCC.2017.8028623

Sensor selection and routing design for state estimation over wireless sensor networks

Yuchi Wu, Takuya Iwaki, Junfeng Wu, Karl Henrik Johansson, Ling Shi

Department of Chemical and Biological Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

1 Citation (Scopus)

Abstract

In this paper, we consider a wireless sensor network that consists of a group of sensor nodes estimating multiple independent LTI systems. Each point-to-point link between the sensor nodes is a slow frequency-flat fading channel and the states of the channel are described by a finite-state Markov channel (FSMC) model. We propose a transmission schedule of the sensors such that the overall estimation error at the remote estimator is minimized. Furthermore, we present an event-based routing scheme with respect to channel state, i.e., online routing among the selected sensors to ensure the zero outage-probability at a constant transmission rate with the least transmission energy. Using channel inversion, we separate the sensor scheduling and routing as two independent steps. Simulation results for a simple wireless sensor network is presented to compare the energy cost with and without event-based routing.

Original language	English
Title of host publication	Proceedings of the 36th Chinese Control Conference, CCC 2017
Editors	Tao Liu, Qianchuan Zhao
Publisher	IEEE Computer Society
Pages	8008-8013
Number of pages	6
ISBN (Electronic)	9789881563934
DOIs	https://doi.org/10.23919/ChiCC.2017.8028623
Publication status	Published - 7 Sept 2017
Event	36th Chinese Control Conference, CCC 2017 - Dalian, China Duration: 26 Jul 2017 → 28 Jul 2017

Publication series

Name	Chinese Control Conference, CCC
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	36th Chinese Control Conference, CCC 2017
Country/Territory	China
City	Dalian
Period	26/07/17 → 28/07/17

Bibliographical note

Publisher Copyright:
© 2017 Technical Committee on Control Theory, CAA.

Keywords

channel inversion
networked control
routing
sensor network
slow fading
state estimation

Access to Document

10.23919/ChiCC.2017.8028623

Cite this

Wu, Y., Iwaki, T., Wu, J., Johansson, K. H., & Shi, L. (2017). Sensor selection and routing design for state estimation over wireless sensor networks. In T. Liu, & Q. Zhao (Eds.), Proceedings of the 36th Chinese Control Conference, CCC 2017 (pp. 8008-8013). Article 8028623 (Chinese Control Conference, CCC). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2017.8028623

@inproceedings{bbf3360a01e94ca0936dc3a6e0b55719,

title = "Sensor selection and routing design for state estimation over wireless sensor networks",

abstract = "In this paper, we consider a wireless sensor network that consists of a group of sensor nodes estimating multiple independent LTI systems. Each point-to-point link between the sensor nodes is a slow frequency-flat fading channel and the states of the channel are described by a finite-state Markov channel (FSMC) model. We propose a transmission schedule of the sensors such that the overall estimation error at the remote estimator is minimized. Furthermore, we present an event-based routing scheme with respect to channel state, i.e., online routing among the selected sensors to ensure the zero outage-probability at a constant transmission rate with the least transmission energy. Using channel inversion, we separate the sensor scheduling and routing as two independent steps. Simulation results for a simple wireless sensor network is presented to compare the energy cost with and without event-based routing.",

keywords = "channel inversion, networked control, routing, sensor network, slow fading, state estimation",

author = "Yuchi Wu and Takuya Iwaki and Junfeng Wu and Johansson, \{Karl Henrik\} and Ling Shi",

note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",

year = "2017",

month = sep,

day = "7",

doi = "10.23919/ChiCC.2017.8028623",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "8008--8013",

editor = "Tao Liu and Qianchuan Zhao",

booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",

}

Wu, Y, Iwaki, T, Wu, J, Johansson, KH & Shi, L 2017, Sensor selection and routing design for state estimation over wireless sensor networks. in T Liu & Q Zhao (eds), Proceedings of the 36th Chinese Control Conference, CCC 2017., 8028623, Chinese Control Conference, CCC, IEEE Computer Society, pp. 8008-8013, 36th Chinese Control Conference, CCC 2017, Dalian, China, 26/07/17. https://doi.org/10.23919/ChiCC.2017.8028623

Sensor selection and routing design for state estimation over wireless sensor networks. / Wu, Yuchi; Iwaki, Takuya; Wu, Junfeng et al.
Proceedings of the 36th Chinese Control Conference, CCC 2017. ed. / Tao Liu; Qianchuan Zhao. IEEE Computer Society, 2017. p. 8008-8013 8028623 (Chinese Control Conference, CCC).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Sensor selection and routing design for state estimation over wireless sensor networks

AU - Wu, Yuchi

AU - Iwaki, Takuya

AU - Wu, Junfeng

AU - Johansson, Karl Henrik

AU - Shi, Ling

PY - 2017/9/7

Y1 - 2017/9/7

N2 - In this paper, we consider a wireless sensor network that consists of a group of sensor nodes estimating multiple independent LTI systems. Each point-to-point link between the sensor nodes is a slow frequency-flat fading channel and the states of the channel are described by a finite-state Markov channel (FSMC) model. We propose a transmission schedule of the sensors such that the overall estimation error at the remote estimator is minimized. Furthermore, we present an event-based routing scheme with respect to channel state, i.e., online routing among the selected sensors to ensure the zero outage-probability at a constant transmission rate with the least transmission energy. Using channel inversion, we separate the sensor scheduling and routing as two independent steps. Simulation results for a simple wireless sensor network is presented to compare the energy cost with and without event-based routing.

AB - In this paper, we consider a wireless sensor network that consists of a group of sensor nodes estimating multiple independent LTI systems. Each point-to-point link between the sensor nodes is a slow frequency-flat fading channel and the states of the channel are described by a finite-state Markov channel (FSMC) model. We propose a transmission schedule of the sensors such that the overall estimation error at the remote estimator is minimized. Furthermore, we present an event-based routing scheme with respect to channel state, i.e., online routing among the selected sensors to ensure the zero outage-probability at a constant transmission rate with the least transmission energy. Using channel inversion, we separate the sensor scheduling and routing as two independent steps. Simulation results for a simple wireless sensor network is presented to compare the energy cost with and without event-based routing.

KW - channel inversion

KW - networked control

KW - routing

KW - sensor network

KW - slow fading

KW - state estimation

UR - https://openalex.org/W2754228581

UR - https://www.scopus.com/pages/publications/85032192689

U2 - 10.23919/ChiCC.2017.8028623

DO - 10.23919/ChiCC.2017.8028623

M3 - Conference Paper published in a book

T3 - Chinese Control Conference, CCC

SP - 8008

EP - 8013

BT - Proceedings of the 36th Chinese Control Conference, CCC 2017

A2 - Liu, Tao

A2 - Zhao, Qianchuan

PB - IEEE Computer Society

T2 - 36th Chinese Control Conference, CCC 2017

Y2 - 26 July 2017 through 28 July 2017

ER -

Sensor selection and routing design for state estimation over wireless sensor networks

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Keywords

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