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

Beuchert, Jonas, Solowjow, Friedrich, Trimpe, Sebastian and Seel, Thomas 2020. Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: an event-triggered learning approach. Sensors 20 (1) , 260. 10.3390/s20010260

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
ISSN:	1424-8220
Date of Acceptance:	30 December 2019
Last Modified:	26 Nov 2024 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/173414

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