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WiFiMon: a mobility analytics platform for building occupancy monitoring and contact tracing using wifi sensing: poster abstract

Published:16 November 2020Publication History

ABSTRACT

With the current COVID-19 pandemic, contact tracing and building occupancy tracking are key components of re-opening policies and quickly containing virus outbreaks. WiFiMon is a network-centric contact tracing method that uses enterprise WiFi networks logs for tracking devices and inferring building occupancy and building contact tracing reports in office and campus settings.

We have built WiFiMon on an analytics platform that leverages the popular open source ELK stack (Elasticsearch, Logstash and Kibana) to process syslog data at large scale and offer users dashboards to quickly monitor building occupancy and generate contract tracing reports.

WiFiMon has been deployed at several UMass campuses gathering daily data from over 5,000 WiFi access points and tens of thousands of users. Our software is freely available for anyone to use and can be found at https://github.com/umassos/elastic-wifitrace.

References

  1. Joel Hellewell et al. 2020. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health (2020)Google ScholarGoogle Scholar
  2. Amee Trivedi, Camellia Zakaria, Rajesh Balan, Prashant Shenoy, 2020. WiFiMon: Network-based Contact Tracing for Infectious Diseases Using Passive WiFi Sensing, arXiv:2005.12045.Google ScholarGoogle Scholar
  3. Dheryta Jaisinghani, et al.. 2018. Experiences & Challenges with Server-Side WiFi Indoor Localization Using Existing Infrastructure. In Proceedings of the 15th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous New York City, NY, Henning Schulzrinne and Pan Li (Eds.). 226--235Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

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      Acceptance Rates

      Overall Acceptance Rate174of867submissions,20%

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