Automated and Private Multimodal IoT Contact Tracing for Covid-19

Guanyao Li, Tianlang He, Shuhan Zhong, and S.-H. Gary Chan

Department of Computer Science and Engineering



Latest News

[2021.12.04] Our paper Tackling Multipath and Biased Training Data for IMU-Assisted BLE Proximity Detection has been accepted by IEEE INFOCOM!
[2021.07.26] Our paper vContact: Private WiFi-based Contact Tracing with Virus Lifespan has been accepted by IEEE Internet of Things Journal!
[2020.09.24] A report about our remarkable results in an open challenge on Bluetooth-based proximity detection is available!
Covid-19 is primarily spread through contact with the virus which may survive on surfaces with lifespan of more than hours. To curb its spread, it is hence of vital importance to detect and quarantine those who have been in contact with the virus for sustained period of time, the so-called close contacts.

The existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has not been any work detecting indirect environmental contact, which is to detect people coming into an area with living virus, i.e., an area previously visited by an infected person within the virus lifespan. In this work, we study, for the first time, automatic contact detection when the virus has a lifespan. Leveraging upon the ubiquity of multiple signals (e.g. WiFi, Bluetooth, INS and possibly GPS.), we propose a novel and fully distributed multimodal IoT solution for automated and private contact tracing.


User's IoT device continuously scans surrounding signal data, such as WiFi, GPS, and Bluetooth. The scanned data are transmitted to the user's phone. All the data are stored locally, unless a user is confirmed infection and consent to upload them. Even in this case, the upload is anonymous and private, and no personal information is used.

Upon positive confirmation in hospital, the patient has the following two possibilities:
  • With the app:
    With the consent of the patient, the health officer may access his/her signal profile (the patient may blank out or filter some parts of the signal profile for personal reasons before sharing it with the officer). Based on that, the officer works with the patient to identify the venues of potential health risks to the public. These risky areas are extracted and labelled with assessed virus lifespan, and the processed signal profile is uploaded to a secure server for other users to match in a distributed manner. Upon matching, users are alerted in private if they have close contact with the virus.
  • Without the app:
    In this case, the confirmed case has to rely on his/her memory of the major venues and their visit time as the manual case. Then some staff will go to the places (the infected areas) to collect offline signal information and label them with the visit time of the patient. These signals, after processing, are then uploaded and matched by the users the same way as in the case above.

  • Exposure data collection.
    Share positive test result.
    Possible exposure notification.
    Our IoT solution is compatible and can be easily integrated with other existing solutions. It has the following strengths:

    [1] T. He, J. Tan, W. Zhuo, M. Printz and S.-H. Chan, "Tackling Multipath and Biased Training Data for IMU-Assisted BLE Proximity Detection," in Proceedings of IEEE International Conference on Computer Communications (IEEE INFOCOM), Virtual Conference, IEEE, 2-5 May, 2022. [paper]
    [2] G. Li, S. Hu, S. Zhong, W. Tsui and S.-H. Chan, "vContact: Private WiFi-based IoT Contact Tracing with Virus Lifespan,'' IEEE Internet of Things Journal, to appear. [Early access in July 2021]
    [3] vContact:Private WiFi-based Contact Tracing with Virus Lifespan, US provisional patent (ref. 63/102,753), 30 June 2020
    [4]Leaderboard of the NIST Pilot TC4TL Challenge (Team name: Contact-Tracing-Project)

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