Indoor occupancy tracking in smart buildings using passive sniffing of probe requests

Vattapparamban, Edwin; Çiftler, Bekir Sait; Güvenç, İsmail; Akkaya, Kemal; Kadri, Abdullah

doi:10.1109/iccw.2016.7503761

Cited by 56 publications

(30 citation statements)

References 14 publications

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“…7 we set k equal to 2 in our case: the resulting cluster centers are the two feature vectors [1 0 0 0 0] and [1 1 0 1 0]. As one can see, the first cluster is [1][2][3][4][5][6][7][8][9][10][11][12] identified by users which are detected for a short period of time (the first bin is present), but never return. We denote this first cluster as the group of passers-by users.…”

Section: Clustering the Lablife Datasetmentioning

confidence: 99%

“…In such phase, each end device broadcasts a probe request management frame to stimulate in-range APs to manifest themselves (replying with a probe reply management frame). Such probe requests are usually broadcasted in sequence on all the available WiFi channels (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). The set of information which is contained in (or can be easily extracted by) probe request frames include the Medium Access Control (MAC) address of the sending device, the Received Signal Strength Indicator (RSSI) out of the transmission of the frame and the Preferred Network List (PNL), that is a list of Service Set IDentifiers (SSID) of the WIFi networks which are already known by the sending device.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The model parameters are empirically estimated for the test environment through an exhaustive measurement campaign. Namely, for all the possible combinations of parameters p 0 and α, the position of the [1][2][3][4][5][6][7][8][9][10][11][12] reference user, that is the vector coordinates of the cell the user lives in, x , is then estimated by solving the following regularized least-square problem.…”

Section: Model-based Localizationmentioning

confidence: 99%

“…In [5], a system composed of eight WiFi sniffers is deployed in an area of about 5000 m 2 . A triangulation-based algorithm is used to localize coarsely the users in eight areas of the experimental area, however no details on the performance of the localization algorithm are reported.…”

Section: Localization and Trackingmentioning

confidence: 99%

“…Computer Communications 117 (2018) [1][2][3][4][5][6][7][8][9][10][11][12] average probe request emission frequency for idle terminals is in the range of one probe every 2-5 minutes. A similar behavior has been observed also in our datasets.…”

Section: Aec Redondi M Cesanamentioning

confidence: 99%

See 4 more Smart Citations

Building up knowledge through passive WiFi probes

Redondi

Cesana

2018

Computer Communications

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Section: Clustering the Lablife Datasetmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Model-based Localizationmentioning

confidence: 99%

Section: Localization and Trackingmentioning

confidence: 99%

Section: Aec Redondi M Cesanamentioning

confidence: 99%

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Building up knowledge through passive WiFi probes

Redondi

Cesana

2018

Computer Communications

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An Online Method for Estimating the Wireless Device Count via Privacy-Preserving Wi-Fi Fingerprinting

Torkamandi

Karkkainen

Ott

2021

Lecture Notes in Computer Science

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Initially envisioned to accelerate association of mobile devices in wireless networks, broadcasting of Wi-Fi probe requests has opened avenues for researchers and network practitioners to exploit information sent out in this type of frames for observing devices' digital footprints and for their tracking. One of the applications for this is crowd estimation. Noticing the privacy risks that this default mode of operation poses, device vendors have introduced MAC address randomization-a privacy preserving technique by which mobile devices periodically generate random hardware addresses contained in probe requests. In this paper, we propose a method for estimating the number of wireless devices in the environment by means of analyzing Wi-Fi probe requests sent by those devices and in spite of MAC address randomization. Our solution extends previous work that uses Wi-Fi fingerprinting based on the timing information of probe requests. The only additional information we extract from probe requests is the MAC address, making our method minimally privacy-invasive. Our estimation method is also nearly real-time. We conduct several experiments to collect wireless measurements in different static environments and we use these measurements to validate our method. Through an extensive analysis and parameter tuning, we show the robustness of our method.

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