000097256 001__ 97256 000097256 005__ 20210902121840.0 000097256 0247_ $$2doi$$a10.3390/electronics9122035 000097256 0248_ $$2sideral$$a121070 000097256 037__ $$aART-2020-121070 000097256 041__ $$aeng 000097256 100__ $$0(orcid)0000-0002-1285-8528$$aVizárraga, Jorge$$uUniversidad de Zaragoza 000097256 245__ $$aDimensionality reduction for smart IoT sensors 000097256 260__ $$c2020 000097256 5060_ $$aAccess copy available to the general public$$fUnrestricted 000097256 5203_ $$aSmart 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. 000097256 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/PTQ-17-09481 000097256 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000097256 590__ $$a2.397$$b2020 000097256 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b93 / 162 = 0.574$$c2020$$dQ3$$eT2 000097256 591__ $$aPHYSICS, APPLIED$$b88 / 160 = 0.55$$c2020$$dQ3$$eT2 000097256 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b145 / 273 = 0.531$$c2020$$dQ3$$eT2 000097256 592__ $$a0.36$$b2020 000097256 593__ $$aComputer Networks and Communications$$c2020$$dQ2 000097256 593__ $$aControl and Systems Engineering$$c2020$$dQ2 000097256 593__ $$aSignal Processing$$c2020$$dQ2 000097256 593__ $$aHardware and Architecture$$c2020$$dQ2 000097256 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ2 000097256 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000097256 700__ $$0(orcid)0000-0001-5316-8171$$aCasas, Roberto$$uUniversidad de Zaragoza 000097256 700__ $$0(orcid)0000-0002-7396-7840$$aMarco, Álvaro 000097256 700__ $$0(orcid)0000-0003-3431-5863$$aBuldain, J. David$$uUniversidad de Zaragoza 000097256 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000097256 773__ $$g9, 12 (2020), [16 pp.]$$pElectronics (Basel)$$tElectronics$$x2079-9292 000097256 8564_ $$s5249782$$uhttps://zaguan.unizar.es/record/97256/files/texto_completo.pdf$$yVersión publicada 000097256 8564_ $$s523830$$uhttps://zaguan.unizar.es/record/97256/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000097256 909CO $$ooai:zaguan.unizar.es:97256$$particulos$$pdriver 000097256 951__ $$a2021-09-02-10:23:14 000097256 980__ $$aARTICLE