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Probabilistic Occupancy Level Estimation Based on Opportunistic Passive Wi-Fi Localisation

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

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Abstract

Location and occupancy are information of major interest for ubiquitous applications such as automated services. In this paper, we describe a novel approach for occupancy estimation based on passive localisation of common Wi-Fi devices (such as smart phones), performed without any modification of the devices. We exploit the fact that devices, on which the Wi-Fi is on, regularly emit messages to communicate with reachable access points (AP). We opportunistically extract Received Signal Strength Indicators (RSSIs) using Wi-Fi sniffers and perform a classic fingerprint-based localisation. The location of devices is then used to infer levels of occupancy at the room level.

We first evaluate our passive localisation system to assess the possibility to perform accurate enough passive localisation. We then propose a generic probabilistic approach to deduce occupancy levels of zones based on the location of devices that could be applied with any localisation results. We present a prototype currently deployed in our lab demonstrating the feasibility of our approach. We evaluate the performance of our approach in a four-person office in which occupancy ground truth was acquired. Our system is easily deployed, scalable and preserves anonymity of users. Finally, we discuss concerns that such an approach may raise and also its potential.

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Notes

  1. 1.

    http://content.time.com/time/interactive/0,31813,2122187,00.html (last accessed on 29/01/2016).

  2. 2.

    http://www.tcpdump.org/ (last accessed on 29/01/2016).

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Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (EeB.NMP.2013-4) under grant agreement number 608790.

The authors would like to thank Martin Klepal and Christian Beder for the provision of the software used for the fingerprint.

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Authors and Affiliations

  1. Nimbus Centre, Cork Institute of Technology, Bishopstown, Cork, Ireland

    Bastien Pietropaoli, Kieran Delaney & Dirk Pesch

  2. IBM Research, Dublin, Ireland

    Joern Ploennigs

Authors
  1. Bastien Pietropaoli
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  2. Kieran Delaney
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  3. Dirk Pesch
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  4. Joern Ploennigs
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Corresponding author

Correspondence to Dirk Pesch .

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Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Pietropaoli, B., Delaney, K., Pesch, D., Ploennigs, J. (2018). Probabilistic Occupancy Level Estimation Based on Opportunistic Passive Wi-Fi Localisation. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_64

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  • DOI: https://doi.org/10.1007/978-3-319-56994-9_64

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