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Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

Eric Lanfer, Thomas Hänel, Roland van Rijswijk-Deij, Nils Aschenbruck

TL;DR

A multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 and BLE signal strength data, measured in four different environments are presented.

Abstract

Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use BLE signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classification model based on BLE and IEEE 802.11 signals. Our approach significantly improves the distance classification and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.

Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

TL;DR

A multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 and BLE signal strength data, measured in four different environments are presented.

Abstract

Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use BLE signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classification model based on BLE and IEEE 802.11 signals. Our approach significantly improves the distance classification and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.
Paper Structure (20 sections, 4 equations, 5 figures, 4 tables)

This paper contains 20 sections, 4 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Bluetooth Low Energy Advertisements
  4. GAEN
  5. IEEE 802.11 probe requests
  6. Signal propagation
  7. Related work
  8. Evaluation studies on proximity tracing
  9. Approaches improving proximity tracing
  10. Approach
  11. Data set
  12. Data collection setup
  13. Measurement environments
  14. Calibration
  15. Evaluation
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Modelled vs. measured attenuation in indoor and outdoor scenarios using BLE channel 39 (2480 MHz). Modelled with log-normal-shadowing-model (LNSM) and two-ray-ground model (TRG)
  • Figure 2: Setup for measurements
  • Figure 3: Bus measurement environment
  • Figure 4: Data flow overview for the entire setup used in this paper, starting with the recording of the signals and ending with the evaluation of the classifiers
  • Figure 5: Comparison of BLE and IEEE 802.11 RSSI values per distance, measured in the meeting ground truth setup, using the OnePlus for sending