Inter-mobile-device distance estimation using network localization algorithms for digital contact logging applications

Clark, Lillian; Papalia, Alan; Carvalho, Jônata Tyska; Mastrostefano, Luca; Krishnamachari, Bhaskar

doi:10.1016/j.smhl.2020.100168

Cited by 7 publications

(9 citation statements)

References 23 publications

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“…A proximity-based privacy-preserving approach with use of smartwatch was described in [97]. The experiments in [98] indicated that in ideal settings, when all RSS measurements are available, a direct estimation provides the best proximity detection and lowest complexity among the studied solutions. Moreover, the context has a more significant effect on the resulting distance estimate matrix than the network localization approach.…”

Section: Proximity Detectionmentioning

confidence: 99%

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

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Section: Proximity Detectionmentioning

confidence: 99%

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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“…Besides the NIST challenge, Clark et al [12] suggested the usage of a network localization algorithm. This approach showed good results, but it requires a central instance that collects all RSSI values received from all devices to compute the algorithm.…”

Section: B Approaches Improving Proximity Tracingmentioning

confidence: 99%

Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

Lanfer,

Hänel,

van Rijswijk-Deij

et al. 2022

Preprint

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Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use Bluetooth Low Energy (BLE) signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proximity estimation, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classification model based on BLE and IEEE 802.11 signals. Our approach significantly improves the distance estimation and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.

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“…Besides the NIST challenge, Clark et al [13] suggested the usage of a network localization algorithm. This approach showed good results, but it requires a central instance that collects all RSSI values received from all devices to compute the algorithm, which will result in a privacy issue.…”

Section: B Approaches Improving Proximity Tracingmentioning

confidence: 99%

Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

Lanfer

Hänel

Rijswijk-Deij

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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Due to the COVID-19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use Bluetooth Low Energy (BLE) signal strength data to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does hardly deliver accurate results. We present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 & 5 GHz) and BLE signal strength data, measured in four different environments. We utilize these data to train machine learning models. The evaluation showed significant improvements in the distance classification and consequently also the contact tracing accuracy. However, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.

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Inter-mobile-device distance estimation using network localization algorithms for digital contact logging applications

Cited by 7 publications

References 23 publications

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

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