Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9936
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Main Title: Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach
Author(s): Beuchert, Jonas
Solowjow, Friedrich
Trimpe, Sebastian
Seel, Thomas
Type: Article
Language Code: en
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.
URI: https://depositonce.tu-berlin.de/handle/11303/11048
http://dx.doi.org/10.14279/depositonce-9936
Issue Date: 2-Jan-2020
Date Available: 29-Apr-2020
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): event-triggered state estimation
Gaussian processes
communication networks
bandwidth limitations
motion tracking
inertial measurement units
body area networks
physiological signals
data transmission protocols
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Sensors
Publisher: MDPI
Publisher Place: Basel
Volume: 20
Issue: 1
Article Number: 260
Publisher DOI: 10.3390/s20010260
EISSN: 1424-8220
Appears in Collections:FG Regelungssysteme » Publications

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