Dimensionality reduction for smart IoT sensors

Vizárraga, Jorge (Universidad de Zaragoza) ; Casas, Roberto (Universidad de Zaragoza) ; Marco, Álvaro ; Buldain, J. David (Universidad de Zaragoza)

Resumen: Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.

Idioma: Inglés

DOI: 10.3390/electronics9122035

Año: 2020

Publicado en: Electronics 9, 12 (2020), [16 pp.]
ISSN: 2079-9292

Factor impacto JCR: 2.397 (2020)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 93 / 162 = 0.574 (2020) - Q3 - T2
Categ. JCR: PHYSICS, APPLIED rank: 88 / 160 = 0.55 (2020) - Q3 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 145 / 273 = 0.531 (2020) - Q3 - T2

Factor impacto SCIMAGO: 0.36 - Computer Networks and Communications (Q2) - Control and Systems Engineering (Q2) - Signal Processing (Q2) - Hardware and Architecture (Q2) - Electrical and Electronic Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MINECO/PTQ-17-09481

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)