Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and fingerprinting
The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphone-based collaborative architecture using neural networks and received signal strength, which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of traditional indoor positioning systems. ; The authors gratefully acknowledge funding from European Union’s Horizon 2020 RIA programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories). The associate editor coordinating the review of this article and approving it for publication was Prof. Name Surname (Corresponding authors: J. Torres-Sospedra and S. Casteleyn).
L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and fingerprinting
The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to alleviate these drawbacks. In this paper, we propose a smartphone-based collaborative architecture using neural networks and received signal strength, which exploits the built-in wireless communication technologies in smartphones and the collaboration between devices to improve traditional positioning systems without additional deployment. Experiments are carried out in two real-world scenarios, demonstrating that our proposed architecture enhances the position accuracy of traditional indoor positioning systems. ; The authors gratefully acknowledge funding from European Union’s Horizon 2020 RIA programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories). The associate editor coordinating the review of this article and approving it for publication was Prof. Name Surname (Corresponding authors: J. Torres-Sospedra and S. Casteleyn).
L/F-CIPS: Collaborative indoor positioning for smartphones with lateration and fingerprinting
Pascacio, Pavel (Autor:in) / Torres-Sospedra, Joaquín (Autor:in) / Casteleyn, Sven (Autor:in) / Lohan, Elena Simona (Autor:in) / Nurmi, Jari (Autor:in)
01.01.2023
doi:10.1109/JSEN.2023.3308147
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Received Signal Strength , Computer architecture , Fingerprint recognition , Engenharia e Tecnologia::Engenharia Eletrotécnica , Neural Networks , Smart phones , Lateration , Collaboration , Collaborative Indoor Positioning , Wireless fidelity , Eletrónica e Informática , Calibration , Fingerprinting , Sensors
DDC:
690
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