Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies
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ORIGINAL ARTICLE
Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies Miguel Ribeiro1
· Nuno Nunes1
· Valentina Nisi2
3 ¨ · Johannes Schoning
Received: 22 January 2020 / Accepted: 12 August 2020 © The Author(s) 2020
Abstract 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 km2 . 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. Keywords Passive sensing · Wi-Fi tracking · Mobility analysis
1 Introduction and motivation Understanding human mobility through wireless sensing and social networks is now commonplace [15, 23]. Using a wide range of sensors, researchers and practitioners can collect data unobtrusively and cost-effectively. Hence, we can now more easily analyze human mobility at unprecedented spatial and temporal resolutions. This information is useful for many domains. For instance, mobility data can be used to understand patterns of human movements in urban settings, [29]. Network connectivity helps establish opportunistic linkages, which improves the Miguel Ribeiro
[email protected] Nuno Nunes
[email protected] Valentina Nisi [email protected] Johannes Sch¨oning [email protected] 1
Instituto Superior T´encico, ITI/LARSyS, ULisbon, Portugal
2
ITI/LARSyS, Universidade da Madeira, Funchal, Portugal
3
Human-Computer Interaction, University of Bremen, ITI/LARSyS, Bremen, Germany
connectivity and location detection of mobile devices [11]. In traffic management, mobility data can be used to provide traffic reports and detecting commuting patterns for planning of transport systems [12, 15]. Similarly, studying contacts among residents on their daily routes helps simulate the dynamics of disease transmission [5] and detect site loads [23], among many other applications. Therefore, collecting mobility data at scale enables dataintensive services operating in real-time as well as offline data mining. These methods are useful to extract data about mobility-related domains such as tourism, visitors, interests, and site loads from social media, and compare it to the traditional sources [1, 10, 18]. By using traditional sources as a term of comparison, we cannot only use it as ground truth to fine-tune the footfall estimation models, but also to see how those models behave in different locations and ag
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