Inter-mobile-device distance estimation using network localization algorithms for digital contact logging applications
| Autor: | Lillian Clark, Bhaskar Krishnamachari, Jonata Tyska Carvalho, Luca Mastrostefano, Alan Papalia |
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| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences 020205 medical informatics Computer science Medicine (miscellaneous) Health Informatics Context (language use) 02 engineering and technology 01 natural sciences law.invention Computer Science - Networking and Internet Architecture Bluetooth Health Information Management law Component (UML) FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Networking and Internet Architecture (cs.NI) Estimation 010401 analytical chemistry Logging 0104 chemical sciences Computer Science Applications Embedding Isomap Mobile device Algorithm Information Systems |
| Zdroj: | Smart Health |
| ISSN: | 2352-6483 |
| DOI: | 10.1016/j.smhl.2020.100168 |
| Popis: | 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. 18 pages, 13 figures, corrected affiliations |
| Databáze: | OpenAIRE |
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