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Direct lightweight temporal compression for wearable sensor data
dc.contributor.author | Klus, Lucie | |
dc.contributor.author | Klus, Roman | |
dc.contributor.author | Lohan, Elena Simona | |
dc.contributor.author | Granell, Carlos | |
dc.contributor.author | Talvitie, Jukka | |
dc.contributor.author | Valkama, Mikko | |
dc.contributor.author | Nurmi, Jari | |
dc.date.accessioned | 2021-03-16T09:25:52Z | |
dc.date.available | 2021-03-16T09:25:52Z | |
dc.date.issued | 2021-02-01 | |
dc.identifier.citation | L. Klus et al., "Direct Lightweight Temporal Compression for Wearable Sensor Data," in IEEE Sensors Letters, vol. 5, no. 2, pp. 1-4, Feb. 2021, Art no. 7000404, doi: 10.1109/LSENS.2021.3051809 | ca_CA |
dc.identifier.issn | 2475-1472 | |
dc.identifier.uri | http://hdl.handle.net/10234/192551 | |
dc.description.abstract | Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this letter, we introduce a lossy compression method for time-series data, named direct lightweight temporal compression (DLTC), enabling energy-efficient data transfer for power-restricted devices. Our method is based on the lightweight temporal compression method, targeting further reconstruction error minimization and complexity reduction. This letter highlights the key advantages of the proposed method and evaluates the method's performance on several sensor-based, time-series data types. We prove that DLTC outperforms the considered benchmark methods in compression efficiency at the same reconstruction error level. | ca_CA |
dc.format.extent | 4 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | IEEE | ca_CA |
dc.relation | A-WEAR - A network for dinamic wearable applications with privacy constraints | ca_CA |
dc.relation.isPartOf | IEEE Sensors Letters, Vol. 5, no. 2, art no. 7000404 (Feb. 2021) | ca_CA |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | upper bound | ca_CA |
dc.subject | benchmark testing | ca_CA |
dc.subject | complexity theory | ca_CA |
dc.subject | performance evaluation | ca_CA |
dc.subject | data compression | ca_CA |
dc.subject | sensor phenomena | ca_CA |
dc.subject | characterization | ca_CA |
dc.subject | sensor signal processing | ca_CA |
dc.subject | direct lightweight temporal compression (DLTC) | ca_CA |
dc.subject | internet of things (IoT) | ca_CA |
dc.subject | lightweight temporal compression (LTC) | ca_CA |
dc.subject | redundancy reduction | ca_CA |
dc.subject | time series | ca_CA |
dc.title | Direct lightweight temporal compression for wearable sensor data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | 10.1109/LSENS.2021.3051809 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://ieeexplore.ieee.org/document/9324996 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.identifier | 10.13039/100010665 ; 10.13039/501100002341 | ca_CA |
project.funder.name | Funder Horizon 2020 Marie Sklodowska Curie Actions | ca_CA |
project.funder.name | Academy of Finland | ca_CA |
oaire.awardNumber | 813278 | ca_CA |
oaire.awardNumber | 323244 | ca_CA |
oaire.awardNumber | 319994 | ca_CA |
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