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

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dc.contributor.author Konak, Abdullah
dc.contributor.author Delattre, Simon
dc.contributor.author Bartolacci, Michael
dc.date.accessioned 2023-12-26T18:47:37Z
dc.date.available 2023-12-26T18:47:37Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 5ef82675-bd80-48d2-b412-abdbd65ddc6d
dc.identifier.uri https://hdl.handle.net/10125/107070
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Location Intelligence Research in System Sciences
dc.subject convolutional neural networks
dc.subject floor plan dataset
dc.subject generative models
dc.subject machine learning
dc.subject signal prediction
dc.title Wireless Signal Prediction using Deep Learning Models for WiFi Positioning and Security Concerns
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Confining 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.extent 9 pages
prism.startingpage 5691
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