From Compression of Wearable-based Data to Effortless Indoor Positoning
- Autores: Lucie Klus
- Directores de la Tesis: Carlos Granell Canut (dir. tes.)
- Lectura: En la Universitat Jaume I ( España ) en 2023
- Idioma: inglés
- Número de páginas: 212
- Tribunal Calificador de la Tesis: Christos Laoudias (presid.), Joaquín Torres Sospedra (secret.), Tobias Feigl (voc.)
- Materias:
- Enlaces
- Tesis en acceso abierto en: TDX
- Resumen
The dissertation focuses on boosting the energy efficiency of IoT and wearable devices by implementing lossy compression techniques onto sensor-based time-series data and into indoor localization paradigms. The thesis deals with lossy compression mechanisms that can be implemented for energy-efficient, delay-sensitive wearable data gathering, transfer, and storage. The novel DLTC compression method ensures optimal compression ratio and reconstruction error trade-off, with minimum complexity and delay. In the scope of indoor positioning, the proposed bit-level, feature-wise, and sample-wise reduction of the radio map support accurate positioning while saving resources in data storage and transfer. The work implements a multi-dimensional compression of the radio map to boost the performance efficiency of the positioning system and proposes a cascade model to compensate for k-NN's drawback of computationally expensive prediction on voluminous datasets.
