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Additional Information: Accurate Extraction of Face-to-Face Proximity Using Smartphones and Bluetooth (WiMAN 2011)

Title Accurate Extraction of Face-to-Face Proximity Using Smartphones and Bluetooth
Authors Shu Liu (Univ. Notre Dame), Aaron Striegel (Univ. Notre Dame)
Contact Author Aaron Striegel, striegel@nd.edu
Venue WIMAN 2011 - 5th Workshop on Wireless Mesh and Ad Hoc Networks (WiMAN), ICCCN 2011 in Maui, Hawaii (Invited Paper)
Tags Bluetooth, Proximity, 802.11, GPS, Social Networks

Abstract

The availability of "always-on" communications has tremendous implications for how people interact socially. In particular, sociologists are interested in the question if such pervasive access increases or decreases face-to-face interactions. Unlike triangulation which seeks to define precise position, the question of face-to-face interactions reduces to one of proximity, i.e. are the individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5m) and can cover a wide variety of environments. Existing approaches such as GPS and WiFi triangulation are insufficient due to those constraints. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption.

Context (Striegel)

The focus of this paper was to explore how accurate we could extract proximity via Bluetooth as part of our upcoming NSF study where we would be monitoring two hundred incoming Notre Dame freshmen with respect to their smart phone usage. The reason why we needed proximity was to measure if proximity of friends significantly biased how one interacting, i.e. the anecdote of whether kids frequently sit next to each other and text despite the fact that they could easily talk to one another. While the anecdote is a bit of a cute aspect, the ability to measure proximity does have several longer-term research benefits in terms of technical development once we observe the larger student population that I comment on a bit below. The key focus of this study was to assess the viability of Bluetooth in the first place.

Our dominant research questions were as follows: number one, was it reasonably accurate or could it be groomed to be more accurate via smoothing or other inputs? Number two, what would the power consumption be, i.e. can we get at least a day of cell phone usage with this on at a reasonably fine-grained interval?

Key Findings

  • Demonstrate that it is viable to use Bluetooth RSSI for 0.5m accuracy for the purposes of detecting proximity
  • Power consumption for Bluetooth detection is exceptionally better than WiFi (802.11) or GPS
  • Objects such as backpacks do make a non-trivial difference in signal strength that can be detected by the light sensor during the day
  • As expected, outdoor environments have a different RSSI mapping versus indoor environments

Notable Quirks

Bluetooth is always a bit quirky and as such, we ended up needing to use CyanogenMod on our phones to be able to get the RSSI signal. Fortunately, RSSI is visible during discovery saving us the pain of pairing 200 devices to each other. Unfortunately, the base Android OS does not allow one to make a device permanently discoverable, hence CyanogenMod. The most recently released version of Android, Ice Cream Sandwich (ICS) - Winter 2011, allows one to make a device permanently discoverable without prompting.

Data Access

As this data is purely experimentally gathered data, we would be happy to share it. It is not terribly exciting though consisting largely of RSSI values and a recorded distance. Reproducing the data for any research group should be relatively trivial, i.e. sample the Bluetooth RSSI periodically during the Discovery phase.

Beyond Our Work

The following comments describe several lingering questions that we did not address in the paper or will be addressing in a future work or just are neat questions in general. The slides from the presentation comment briefly on this:

  • Is proximity perhaps the next frontier in terms of location? Is proximity perhaps an equally important component versus location or is just a bonus?
  • Is proximity that critical for how we consume content? Could we use that data to pro-actively improve caching?
  • Is proximity a concern for privacy / leakage of information?
  • How accurate do you need to be with proximity? We are aiming for a half meter accuracy? Is it good enough to simply differentiate between near and far?

Acknowledgments

  • National Science Foundation, Award IIS-0968529, Link
    • This material is based upon work supported by the National Science Foundation under Grant No. IIS-0968529.
    • Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
  • Sprint, Press Release
  • Wireless Institute at Notre Dame
  • Collaborators: David Hachen, Omar Lizardo, Christian Poellabauer

Related Work (ND)

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PowerPointpptx WiMan-BTProx-Talk.pptx r1 manage 1266.6 K 2012-01-17 - 15:35 AaronStriegel Presentation slides at WIMAN 2011
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Topic revision: r1 - 2012-01-17 - AaronStriegel
 
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