2022
DOI: 10.3390/s22155609
|View full text |Cite
|
Sign up to set email alerts
|

User-Centric Proximity Estimation Using Smartphone Radio Fingerprinting

Abstract: The integration of infectious disease modeling with the data collection process is crucial to reach its maximum potential, and remains a significant research challenge. Ensuring a solid empirical foundation for models used to fill gaps in data and knowledge is of paramount importance. Personal wireless devices, such as smartphones, smartwatches and wireless bracelets, can serve as a means of bridging the gap between empirical data and the mathematical modeling of human contacts and networking. In this paper, w… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
3
1

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(2 citation statements)
references
References 54 publications
0
2
0
Order By: Relevance
“…A deep neural network (DNN) [49] or a SVM [50] can be used to detect spoofing. Gaussian mixture models with infinite parameters can improve authentication's secrecy [51], [52]. -Blockchain aspect: IoT devices enabled by BLE have become ubiquitous.…”
Section: Research Challenges and Future Opportunitiesmentioning
confidence: 99%
“…A deep neural network (DNN) [49] or a SVM [50] can be used to detect spoofing. Gaussian mixture models with infinite parameters can improve authentication's secrecy [51], [52]. -Blockchain aspect: IoT devices enabled by BLE have become ubiquitous.…”
Section: Research Challenges and Future Opportunitiesmentioning
confidence: 99%
“…As mentioned in Section 1.4, a way around this problem that has garnered considerable attention is the so-called de-randomization approach for PRs. In this approach [14][15][16][17][18][19][20][21][22][23][24][25][26], information from digital frame content such as the frame Sequence Number, particular Information Elements (IE), Preferred Network List, and certain time delay measurements derived from these quantities can be analyzed statistically to group together PRs arising from a unique client, thus undoing the effect of the MAC randomization. Though rigorous and often quite effective, a recognized drawback of this approach is that wireless device manufacturers are perpetually on the lookout for such security loopholes, which the next generation of devices is likely to close.…”
Section: Related Workmentioning
confidence: 99%