Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach
| Autor: | Friedrich Solowjow, Sebastian Trimpe, Jonas Beuchert, Thomas Seel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
event-triggered state estimation data transmission protocols Computer science Real-time computing Wearable computer Gaussian processes body area networks 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Upper and lower bounds Article physiological signals Analytical Chemistry symbols.namesake 020901 industrial engineering & automation communication networks lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Gaussian process 010401 analytical chemistry Bandwidth (signal processing) bandwidth limitations Telecommunications network Atomic and Molecular Physics and Optics 0104 chemical sciences inertial measurement units symbols motion tracking ddc:620 Wireless sensor network Data compression |
| Zdroj: | Sensors Volume 20 Issue 1 Sensors (Basel, Switzerland) Sensors 20(1), 260 (2020). doi:10.3390/s20010260 special issue: "Special Issue "Inertial Sensors" / Special Issue Editors: Dr. Thomas Seel, Guest Editor; Dr. Manon Kok, Guest Editor; Dr. Ryan S. McGinnis, Guest Editor" Sensors, Vol 20, Iss 1, p 260 (2020) |
| DOI: | 10.3390/s20010260 |
| Popis: | 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&ndash 70%, which implies that two to three times more sensor nodes could be used at the same bandwidth. |
| Databáze: | OpenAIRE |
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