WiFiTrace

Abstract

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional methods, rather than to supplant them with new technology. We designed an efficient graph algorithm to scale our approach to large networks with tens of thousands of users. The graph-based approach outperforms an indexed PostgresSQL in memory by at least 4.5X without any index update overheads or blocking. We have implemented a full prototype of our system and deployed it on two large university campuses. We validated our approach and demonstrate its efficacy using case studies and detailed experiments using real-world WiFi datasets.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses

“…We compared the features of contact networks constructed with WiMob , against networks constructed with En using data from GT for Fall semester of 2019 (August 19–December 14), prior to any COVID-19 reported cases in the U.S. En approximates contact based on students enrolling for classes that could potentially collocate them in the same room during lectures. WiMob infers contact when any two individuals actually collocate near the same WiFi access point ( 41 , 42 ) for extended period (see explanation in Supplementary Material, WiFi Mobility ). We found that WiMob rendered new insight into contact on campus that was invisible to the En methodology.…”

“…By contrast, WIMOB is more grounded in community behavior as it captures multiple scheduled and serendipitous contact situations dynamically over the semester. We compared the features of contact networks constructed with (41,42) for extended period (see explanation in Supplementary Material, WiFi Mobility). We found that WIMOB rendered new insight into contact on campus that was invisible to the EN methodology.…”

“…At the same time, studies to infer campus mobility networks have relied on accessing user devices with specialized data logging applications (e.g., contact tracing mobile apps) ( 35 – 38 ), but these approaches are typically constrained for disease modeling because they require mass adoption to represent the entire community and continuous maintenance of software is needed to capture longitudinal behavior changes. In contrast, our work repurposes already existing managed WiFi networks to model mobility, which provides room level granularity for mobility ( 39 – 42 ) and consequently indicates proximate contact ( 23 ). Much like En , universities internally archive such data over a long term for other purposes and do not need to install any additional surveillance infrastructure to access it.…”

Do WiFi Probe-Requests Reveal Your Trajectory?

“…With mobile phones becoming proxies for human presence, network resources have been exploited to investigate users' mobility [1]. Spatio-temporal mobility datasets are nowadays acknowledged as a common tool to study users' trajectories: e.g., mobile phone records [2], WiFi [3], [4], and BLE data [5]. Most require access to users' devices, network infrastructure, or volunteer recruiting, while some lack scalability.…”

Physical Tampering Detection Using Single COTS Wi-Fi Endpoint

“…In the midst of pandemic, Wi-Fi has been among the communication techniques explored for digital contact tracing, as it has the largest signal coverage in most indoor environments and a nonintrusive proximity calculation can be done to detect close contact [6,7]. In fact, the method of [8] has already been deployed on university campuses for epidemic prevention.…”