Direct lightweight temporal compression for wearable sensor data
Visualitza/
Impacte
Scholar |
Altres documents de l'autoria: Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Granell, Carlos; Talvitie, Jukka; Valkama, Mikko; Nurmi, Jari
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONMetadades
Títol
Direct lightweight temporal compression for wearable sensor dataAutoria
Data de publicació
2021-02-01Editor
IEEEISSN
2475-1472Cita bibliogràfica
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.2021.3051809Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://ieeexplore.ieee.org/document/9324996Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
upper bound | benchmark testing | complexity theory | performance evaluation | data compression | sensor phenomena | characterization | sensor signal processing | direct lightweight temporal compression (DLTC) | internet of things (IoT) | lightweight temporal compression (LTC) | redundancy reduction | time series
Resum
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 ... [+]
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. [-]
Publicat a
IEEE Sensors Letters, Vol. 5, no. 2, art no. 7000404 (Feb. 2021)Entitat finançadora
Funder Horizon 2020 Marie Sklodowska Curie Actions | Academy of Finland
Identificador de l'entitat finançadora
10.13039/100010665 ; 10.13039/501100002341
Codi del projecte o subvenció
813278 | 323244 | 319994
Títol del projecte o subvenció
A-WEAR - A network for dinamic wearable applications with privacy constraints
Drets d'accés
info:eu-repo/semantics/openAccess
Apareix a les col.leccions
- INIT_Articles [754]
Els següents fitxers sobre la llicència estan associats a aquest element: