Online offline learning for sound-based indoor localization using low-cost hardware

  • Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.

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Author:Rüdiger Machhamer, Matthias Dziubany, Levin Czenkusch, Hendrik Laux, Anke Schmeink, Klaus-Uwe Gollmer, Stefan Naumann, Guido Dartmann
Parent Title (English):IEEE Access
Document Type:Article (specialist journals)
Date of OPUS upload:2022/09/01
Date of first Publication:2019/10/16
Publishing University:Hochschule Trier
Release Date:2022/09/05
Tag:fingerprint recognition; incremental learning; indoor localization; internet of things; learning vector quantization; machine learning; online learning; signal processing
GND Keyword:Online-Algorithmus; Autonomer Roboter; Maschinelles Lernen
Page Number:19
First Page:155088
Last Page:155106
Departments:Institute / ISS - Institut für Softwaresysteme in Wirtschaft, Umwelt und Verwaltung
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International