Description
Identifying which individuals have been in close-enough proximity for long-enough to constitute an epidemiologically significant contact. The COVID-19 era turned this from a public-health niche into a major wireless-sensing application. Two major architectures dominate: BLE-based decentralized exposure notification (GAEN-style), and infrastructure-based proximity tracking that consumes BLE / WiFi observations from fixed sensors plus an indoor map. The latter framing connects directly to indoor-localization and occupancy-estimation.
Why it's hard
- Distance estimation from BLE RSSI is noisy and direction-of-arrival-dependent.
- "Significant contact" is an epidemiological judgement, not a fixed distance/duration threshold.
- Privacy-vs-utility tension is severe; centralized infrastructure approaches are politically fraught.
- Multi-modal fusion across BLE, WiFi probe-requests, and indoor maps produces brittle systems.
- Cross-jurisdiction interoperability is essentially absent.
Common approaches
- BLE peer-to-peer decentralized exposure notification (Apple/Google framework).
- Infrastructure-based BLE/WiFi capture combined with indoor-map proximity reasoning.
- IndoorGML-grounded person-to-person and person-to-place tracking.
- RSSI-to-distance calibration plus duration thresholds for contact event definition.