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Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies
In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rur...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495138/ https://www.ncbi.nlm.nih.gov/pubmed/32958999 http://dx.doi.org/10.1007/s00779-020-01441-z |
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author | Ribeiro, Miguel Nunes, Nuno Nisi, Valentina Schöning, Johannes |
author_facet | Ribeiro, Miguel Nunes, Nuno Nisi, Valentina Schöning, Johannes |
author_sort | Ribeiro, Miguel |
collection | PubMed |
description | In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km(2). In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data. |
format | Online Article Text |
id | pubmed-7495138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-74951382020-09-17 Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies Ribeiro, Miguel Nunes, Nuno Nisi, Valentina Schöning, Johannes Pers Ubiquitous Comput Original Article In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km(2). In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data. Springer London 2020-09-17 2022 /pmc/articles/PMC7495138/ /pubmed/32958999 http://dx.doi.org/10.1007/s00779-020-01441-z Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Ribeiro, Miguel Nunes, Nuno Nisi, Valentina Schöning, Johannes Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies |
title | Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies |
title_full | Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies |
title_fullStr | Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies |
title_full_unstemmed | Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies |
title_short | Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies |
title_sort | passive wi-fi monitoring in the wild: a long-term study across multiple location typologies |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495138/ https://www.ncbi.nlm.nih.gov/pubmed/32958999 http://dx.doi.org/10.1007/s00779-020-01441-z |
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