2023/10/25 by Ioannis Nasios, Nasios, Ioannis, Konstantinos Vogklis +8
Computer Science · Engineering · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Mobile Crowdsensing and Crowdsourcing
paper · pdf · doi:10.48550/arxiv.2310.16496
openalex publication_date 2023/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In the present era of sustainable innovation, the circular economy paradigm dictates the optimal use and exploitation of existing finite resources. At the same time, the transition to smart infrastructures requires considerable investment in capital, resources and people. In this work, we present a general machine learning approach for offering indoor location awareness without the need to invest in additional and specialised hardware. We explore use cases where visitors equipped with their smart phone would interact with the available WiFi infrastructure to estimate their location, since the indoor requirement poses a limitation to standard GPS solutions. Results have shown that the proposed approach achieves a less than 2m accuracy and the model is resilient even in the case where a substantial number of BSSIDs are dropped.