Inter-Mobile-Device Distance Estimation using Network Localization Algorithms for Digital Contact Logging Applications

TL;DR

This paper evaluates various network localization algorithms for estimating inter-device distances using Bluetooth signals, crucial for digital contact tracing, especially under noisy or incomplete measurement conditions.

Contribution

It compares multiple algorithms for Bluetooth-based distance estimation, highlighting the spring model's superior accuracy in challenging measurement environments.

Findings

Spring model outperforms other algorithms in noisy conditions

Network localization algorithms are effective for contact logging

Direct RSSI-based estimation is best with clean measurements

Abstract

Mobile applications are being developed for automated logging of contacts via Bluetooth to help scale up digital contact tracing efforts in the context of the ongoing COVID-19 pandemic. A useful component of such applications is inter-device distance estimation, which can be formulated as a network localization problem. We survey several approaches and evaluate the performance of each on real and simulated Bluetooth Low Energy (BLE) measurement datasets with respect to both distance estimate accuracy and the proximity detection problem. We investigate the effects of obstructions like pockets, differences between device models, and the environment (i.e. indoors or outdoors) on performance. We conclude that while direct estimation can provide the best proximity detection when Received Signal Strength Indicator (RSSI) measurements are available, network localization algorithms like Isomap, Local Linear Embedding, and the spring model outperform direct estimation in the presence of missing or very noisy measurements. The spring model consistently achieves the best distance estimation accuracy.