Collaborative techniques for indoor positioning systems
- Autores: Pavel Pascacio De Los Santos
- Directores de la Tesis: Sven Casteleyn (dir. tes.)
, Joaquín Torres Sospedra (dir. tes.) - Lectura: En la Universitat Jaume I ( España ) en 2023
- Idioma: español
- Tribunal Calificador de la Tesis: Maarten Bert J. Weyn (presid.) , Inmaculada Remolar Quintana (secret.) , Estefanía Muñoz Díaz Ropero (voc.)
- Programa de doctorado: Programa de Doctorado Europeo Conjunto Marie Sklodowska-Curie en Aplicaciones Dinámicas de Accesorios Inteligentes con Restricciones de Privacidad / European Joint Doctorate Marie Sklodowska-Curie in A Network for Dynamic Wearable Applications with Privacy Constraints por la Universidad Jaume I de Castellón; Tampereen Teknillinen Yliopisto(Finlandia); Universitatea Politehnica din Bucuresti (UPB)(Rumania); Università degli Studi Mediterranea di Reggio Calabria(Italia) y Vysoké ucení technické v
- Menciones:
- Internacional
- Calificación: Sobresaliente Cum Laude
- Enlaces
- Tesis en acceso abierto en: TDX
- Resumen
This doctoral thesis focuses on developing and evaluating mobile device-based collaborative techniques to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. During the research, first, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to obtain a state-of-the-art; second, extensive experimental data collections considering mobile devices and collaborative scenarios were performed to create a mobile device-based BLE database and BLE and Wi-Fi radio maps for testing our collaborative and non-collaborative indoor positioning approaches; third, traditional methods to estimate distance and position were evaluated to present their limitations and challenges and two novel approaches to improve distance and positioning accuracy were proposed; finally, our proposed CIPSs using Multilayer Perceptron Artificial Neural Networks were developed to enhance the accuracy of BLE¿RSSI lateration and fingerprinting-KNN methods and evaluated under real-world conditions to demonstrate its feasibility and benefits.
