Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring

Wentao Wang; Jianing Wang; Bin Han; Guangyou Mu; Jingtang Xu; Yang Li

doi:10.1117/12.2613189

Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring

Wentao Wang^*
, Jianing Wang
, Bin Han
, Guangyou Mu
, Jingtang Xu
, Yang Li

^*Corresponding author for this work

University of Michigan, Ann Arbor
China Shanghai Railway Certification (Group) Co. Ltd

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

Abstract

Wireless sensor offers impressive functionality compared with traditional wired sensor, including straightforward installation, lightweight, low-cost circuits, high-performance microcontroller and sensors, easy maintenance, and flexibility and scalability for projects. While, there are various challenges to be considered in wireless sensors such as energy efficiency, communication rate, reliability, and data loss. And these drawbacks constrain its applications on specific projects, e.g., long-term monitoring for infrastructures. To overcome the aforementioned drawbacks, this study aims to explore a comprehensive solution from two sides: both the hardware achievement and the communication algorithm. Ultra-low power Urbano wireless sensing node with a high-performance computational microcontroller is proposed as the backbone of the wireless sensing system. Satellite communication is employed to ensure the proposed wireless sensing node is completely autonomous to suit a wider range of fields without the requirements of additional base stations (that host single-board computers). In addition, this paper advances compressive sampling (CS) framework as an alternative to traditional Nyquist/Shannon sampling for SHM. An embedded CS-based algorithm is developed to compress the acquired time-history signal, save the storage space of Urbano, reduce data rate requirements, and ensure the accuracy of data via ultra-low rate communication.

Original language	English
Title of host publication	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022
Editors	Daniele Zonta, Daniele Zonta, Branko Glisic, Zhongqing Su
Publisher	SPIE
ISBN (Electronic)	9781510649675
DOIs	https://doi.org/10.1117/12.2613189
State	Published - 2022
Externally published	Yes
Event	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022 - Virtual, Online Duration: 4 Apr 2022 → 10 Apr 2022

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12046
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022
City	Virtual, Online
Period	4/04/22 → 10/04/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Hong Kong-Zhuhai-Macao Bridge
compressive sampling
structural health monitoring
wireless sensing

Access to Document

10.1117/12.2613189

Cite this

Wang, W., Wang, J., Han, B., Mu, G., Xu, J., & Li, Y. (2022). Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring. In D. Zonta, D. Zonta, B. Glisic, & Z. Su (Eds.), Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022 Article 1204603 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12046). SPIE. https://doi.org/10.1117/12.2613189

Wang, Wentao ; Wang, Jianing ; Han, Bin et al. / Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring. Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022. editor / Daniele Zonta ; Daniele Zonta ; Branko Glisic ; Zhongqing Su. SPIE, 2022. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{373ff43391c744aea0020700294a7977,

title = "Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring",

abstract = "Wireless sensor offers impressive functionality compared with traditional wired sensor, including straightforward installation, lightweight, low-cost circuits, high-performance microcontroller and sensors, easy maintenance, and flexibility and scalability for projects. While, there are various challenges to be considered in wireless sensors such as energy efficiency, communication rate, reliability, and data loss. And these drawbacks constrain its applications on specific projects, e.g., long-term monitoring for infrastructures. To overcome the aforementioned drawbacks, this study aims to explore a comprehensive solution from two sides: both the hardware achievement and the communication algorithm. Ultra-low power Urbano wireless sensing node with a high-performance computational microcontroller is proposed as the backbone of the wireless sensing system. Satellite communication is employed to ensure the proposed wireless sensing node is completely autonomous to suit a wider range of fields without the requirements of additional base stations (that host single-board computers). In addition, this paper advances compressive sampling (CS) framework as an alternative to traditional Nyquist/Shannon sampling for SHM. An embedded CS-based algorithm is developed to compress the acquired time-history signal, save the storage space of Urbano, reduce data rate requirements, and ensure the accuracy of data via ultra-low rate communication.",

keywords = "Hong Kong-Zhuhai-Macao Bridge, compressive sampling, structural health monitoring, wireless sensing",

author = "Wentao Wang and Jianing Wang and Bin Han and Guangyou Mu and Jingtang Xu and Yang Li",

note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022 ; Conference date: 04-04-2022 Through 10-04-2022",

year = "2022",

doi = "10.1117/12.2613189",

language = "英语",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Daniele Zonta and Daniele Zonta and Branko Glisic and Zhongqing Su",

booktitle = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022",

address = "美国",

}

Wang, W, Wang, J, Han, B, Mu, G, Xu, J & Li, Y 2022, Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring. in D Zonta, D Zonta, B Glisic & Z Su (eds), Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022., 1204603, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12046, SPIE, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022, Virtual, Online, 4/04/22. https://doi.org/10.1117/12.2613189

Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring. / Wang, Wentao; Wang, Jianing; Han, Bin et al.
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022. ed. / Daniele Zonta; Daniele Zonta; Branko Glisic; Zhongqing Su. SPIE, 2022. 1204603 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12046).

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

TY - GEN

T1 - Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring

AU - Wang, Wentao

AU - Wang, Jianing

AU - Han, Bin

AU - Mu, Guangyou

AU - Xu, Jingtang

AU - Li, Yang

PY - 2022

Y1 - 2022

N2 - Wireless sensor offers impressive functionality compared with traditional wired sensor, including straightforward installation, lightweight, low-cost circuits, high-performance microcontroller and sensors, easy maintenance, and flexibility and scalability for projects. While, there are various challenges to be considered in wireless sensors such as energy efficiency, communication rate, reliability, and data loss. And these drawbacks constrain its applications on specific projects, e.g., long-term monitoring for infrastructures. To overcome the aforementioned drawbacks, this study aims to explore a comprehensive solution from two sides: both the hardware achievement and the communication algorithm. Ultra-low power Urbano wireless sensing node with a high-performance computational microcontroller is proposed as the backbone of the wireless sensing system. Satellite communication is employed to ensure the proposed wireless sensing node is completely autonomous to suit a wider range of fields without the requirements of additional base stations (that host single-board computers). In addition, this paper advances compressive sampling (CS) framework as an alternative to traditional Nyquist/Shannon sampling for SHM. An embedded CS-based algorithm is developed to compress the acquired time-history signal, save the storage space of Urbano, reduce data rate requirements, and ensure the accuracy of data via ultra-low rate communication.

AB - Wireless sensor offers impressive functionality compared with traditional wired sensor, including straightforward installation, lightweight, low-cost circuits, high-performance microcontroller and sensors, easy maintenance, and flexibility and scalability for projects. While, there are various challenges to be considered in wireless sensors such as energy efficiency, communication rate, reliability, and data loss. And these drawbacks constrain its applications on specific projects, e.g., long-term monitoring for infrastructures. To overcome the aforementioned drawbacks, this study aims to explore a comprehensive solution from two sides: both the hardware achievement and the communication algorithm. Ultra-low power Urbano wireless sensing node with a high-performance computational microcontroller is proposed as the backbone of the wireless sensing system. Satellite communication is employed to ensure the proposed wireless sensing node is completely autonomous to suit a wider range of fields without the requirements of additional base stations (that host single-board computers). In addition, this paper advances compressive sampling (CS) framework as an alternative to traditional Nyquist/Shannon sampling for SHM. An embedded CS-based algorithm is developed to compress the acquired time-history signal, save the storage space of Urbano, reduce data rate requirements, and ensure the accuracy of data via ultra-low rate communication.

KW - Hong Kong-Zhuhai-Macao Bridge

KW - compressive sampling

KW - structural health monitoring

KW - wireless sensing

UR - https://www.scopus.com/pages/publications/85132015566

U2 - 10.1117/12.2613189

DO - 10.1117/12.2613189

M3 - 会议稿件

AN - SCOPUS:85132015566

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022

A2 - Zonta, Daniele

A2 - Glisic, Branko

A2 - Su, Zhongqing

PB - SPIE

T2 - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022

Y2 - 4 April 2022 through 10 April 2022

ER -

Wang W, Wang J, Han B, Mu G, Xu J, Li Y. Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring. In Zonta D, Zonta D, Glisic B, Su Z, editors, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022. SPIE. 2022. 1204603. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2613189

Embedded Compressive Sampling (CS) Algorithm under Ultra-low Rate Wireless Communication for Long-term Bridge Monitoring

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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