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Real-time localisation system for GPS-denied open areas using smart street furniture

Nassar, M.A., Luxford, L., Cole, P., Oatley, G. and Koutsakis, P.ORCID: 0000-0002-4168-0888 (2021) Real-time localisation system for GPS-denied open areas using smart street furniture. Simulation Modelling Practice and Theory, 112 . Art. 102372.

Link to Published Version: https://doi.org/10.1016/j.simpat.2021.102372
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Abstract

Wifi-based localisation systems have gained significant interest with many researchers proposing different localisation techniques using publicly available datasets. However, these datasets are limited because they only contain Wifi fingerprints collected and labelled by users, and they are restricted to indoor locations.

We have generated the first Wifi-based localisation datasets for a GPS-denied open area. We selected a busy open area at Murdoch University to generate the datasets using so-called “smart bins”, which are rubbish bins that we enabled to work as access points. The data gathered consists of two different datasets. In the first, four users generated labelled WiFi fingerprints for all available Reference Points using four different smartphones. The second dataset includes 2450865 auto-generated rows received from more than 1000 devices.

We have developed a light-weight algorithm to label the second dataset from the first and we proposed a localisation approach that converts the second dataset from asynchronous format to synchronous, applies feature engineering and a deep learning classifier. Finally, we have demonstrated via simulations that by using this approach we achieve higher prediction accuracy, with up to 19% average improvement, compared with using only the fingerprint dataset.

Item Type: Journal Article
Murdoch Affiliation(s): IT, Media and Communications
Publisher: Elsevier
Copyright: © 2021 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/61652
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