Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Jonas Beuchert
  • Friedrich Solowjow
  • Sebastian Trimpe
  • Thomas Seel

External Research Organisations

  • University of Oxford
  • Max Planck Institute for Intelligent Systems
  • Technische Universität Berlin
View graph of relations

Details

Original languageEnglish
Article number260
Number of pages19
JournalSensors
Volume20
Issue number1
Publication statusPublished - 2020
Externally publishedYes

Abstract

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.

Keywords

    Bandwidth limitations, Body area networks, Communication networks, Data transmission protocols, Event-triggered state estimation, Gaussian processes, Inertial measurement units, Motion tracking, Physiological signals

ASJC Scopus subject areas

Cite this

Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach. / Beuchert, Jonas; Solowjow, Friedrich; Trimpe, Sebastian et al.
In: Sensors, Vol. 20, No. 1, 260, 2020.

Research output: Contribution to journalArticleResearchpeer review

Download
@article{624a48156957419fbe7cb781075b9460,
title = "Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach",
abstract = "Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.",
keywords = "Bandwidth limitations, Body area networks, Communication networks, Data transmission protocols, Event-triggered state estimation, Gaussian processes, Inertial measurement units, Motion tracking, Physiological signals",
author = "Jonas Beuchert and Friedrich Solowjow and Sebastian Trimpe and Thomas Seel",
note = "Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2020",
doi = "10.3390/s20010260",
language = "English",
volume = "20",
journal = "Sensors",
issn = "1424-3210",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "1",

}

Download

TY - JOUR

T1 - Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns

T2 - An event-triggered learning approach

AU - Beuchert, Jonas

AU - Solowjow, Friedrich

AU - Trimpe, Sebastian

AU - Seel, Thomas

N1 - Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020

Y1 - 2020

N2 - Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.

AB - Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.

KW - Bandwidth limitations

KW - Body area networks

KW - Communication networks

KW - Data transmission protocols

KW - Event-triggered state estimation

KW - Gaussian processes

KW - Inertial measurement units

KW - Motion tracking

KW - Physiological signals

UR - http://www.scopus.com/inward/record.url?scp=85077566492&partnerID=8YFLogxK

U2 - 10.3390/s20010260

DO - 10.3390/s20010260

M3 - Article

VL - 20

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 1

M1 - 260

ER -

By the same author(s)