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Wireless Signal Prediction using Deep Learning Models for WiFi Positioning and Security Concerns

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dc.contributor.authorKonak, Abdullah
dc.contributor.authorDelattre, Simon
dc.contributor.authorBartolacci, Michael
dc.date.accessioned2023-12-26T18:47:37Z
dc.date.available2023-12-26T18:47:37Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.685
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other5ef82675-bd80-48d2-b412-abdbd65ddc6d
dc.identifier.urihttps://hdl.handle.net/10125/107070
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLocation Intelligence Research in System Sciences
dc.subjectconvolutional neural networks
dc.subjectfloor plan dataset
dc.subjectgenerative models
dc.subjectmachine learning
dc.subjectsignal prediction
dc.titleWireless Signal Prediction using Deep Learning Models for WiFi Positioning and Security Concerns
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractConfining wireless signals (WiFi) in specific areas of indoor spaces is an efficient way to protect these networks against unwanted access. Unfortunately, these same WiFi signals can be utilized to track the location of mobile handsets. There is an apparent tradeoff between securing the range of such signals and their use for indoor geolocation purposes. The modeling of wireless signal coverage for both security and geolocation purposes in areas where measurements are difficult to record can be a daunting task. We utilized a deep autoregressive model and a convolutional neural network model trained on a synthetic floor plan dataset to accurately extrapolate signal coverage across such spaces without using specific information about antennae placements or floor plan designs. Computational experiments showed that these data-driven approaches were able to fill the gaps in signal coverage maps accurately.
dcterms.extent9 pages
prism.startingpage5691

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