Details
| Original language | English |
|---|---|
| Article number | 260 |
| Number of pages | 19 |
| Journal | Sensors |
| Volume | 20 |
| Issue number | 1 |
| Publication status | Published - 2020 |
| Externally published | Yes |
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
- Chemistry(all)
- Analytical Chemistry
- Computer Science(all)
- Information Systems
- Physics and Astronomy(all)
- Instrumentation
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Electrical and Electronic Engineering
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
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In: Sensors, Vol. 20, No. 1, 260, 2020.
Research output: Contribution to journal › Article › Research › peer review
}
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 -