Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost Hardware

• Published in: IEEE access Vol. 7; pp. 155088 - 155106
• Main Authors: Machhamer, Rudiger, Dziubany, Matthias, Czenkusch, Levin, Laux, Hendrik, Schmeink, Anke, Gollmer, Klaus-Uwe, Naumann, Stefan, Dartmann, Guido
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer networks; Cyber-physical systems; Distance learning; Ducts; Embedded systems; Fingerprint recognition; Hardware; incremental learning; Indoor environments; indoor localization; Internet of Things; IP networks; learning vector quantization; Localization; Logistics; Loudspeakers; Low cost; Machine learning; Machine learning algorithms; Microphones; online learning; Optimization; Signal processing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2947581

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.