TL;DR
This paper presents a BLE-based smart contact tracing system that uses machine learning to classify contact risk levels, preserves user privacy, and provides real-time alerts, improving upon manual tracing methods.
Contribution
The paper introduces a novel BLE-based contact tracing system with machine learning classification and privacy preservation, along with extensive real-world experiments and publicly available data.
Findings
Decision tree classifier achieves highest accuracy among tested methods.
System accurately estimates distances using RSS for social distancing alerts.
Extensive real-life data supports system effectiveness and classifier performance.
Abstract
Contact tracing is of paramount importance when it comes to preventing the spreading of infectious diseases. Contact tracing is usually performed manually by authorized personnel. Manual contact tracing is an inefficient, error-prone, time-consuming process of limited utility to the population at large as those in close contact with infected individuals are informed hours, if not days, later. This paper introduces an alternative way to manual contact tracing. The proposed Smart Contact Tracing (SCT) system utilizes the smartphone's Bluetooth Low Energy (BLE) signals and machine learning classifier to accurately and quickly determined the contact profile. SCT's contribution is two-fold: a) classification of the user's contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communications protocol. SCT leverages BLE's non-connectable…
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