Smartphone bluetooth based social sensing
Abstract
The increasing mobile technology raises a new paradigm of people-centric sensing using today's smartphones. Towards this paradigm, we present "CoSoBlue", a novel framework for Bluetooth based social sensing. In CoSoBlue, we propose novel Bluetooth semantic and statistical features, in addition to count and similarity features, and apply these discriminative features to infer context and compute sociability. We evaluate CoSoBlue on two Bluetooth datasets: (1) the longitudinal MIT friend-and-family dataset with 9+ millions records, and (2) a new 2-month dataset with ground-truth labels collected using our own developed Android app. Our primilinary experiments show CoSoBlue's efficacy on Bluetooth based social and context sensing.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The embedded Bluetooth sensor can record individuals' local proximity information, such as the nearby Bluetooth device count (NBDC) that includes the Bluetooth signal of other phone users.
“The embedded Bluetooth sensor can be used to record individuals’ local proximity information, such as the nearby Bluetooth device count (NBDC) that includes the Bluetooth signal of other phone users [7].”
Cites
Reality mining: sensing complex social systems
Eagle, N., & (Sandy) Pentland, A. (2006) · Personal and Ubiquitous Computing · doi:10.1007/s00779-005-0046-3 · Cited by 2,772 · see citing works
Social fMRI: Investigating and shaping social mechanisms in the real world
Aharony, N., Pan, W., Ip, C., Khayal, I. S., & Pentland, A. (2011) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2011.09.004 · Cited by 458 · see citing works
Bluetooth based collaborative crowd density estimation with mobile phones
Weppner, J., & Lukowicz, P. (2013) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2013.6526732 · Cited by 162 · see citing works
Exploring Social Context with the Wireless Rope
Nicolai, T., Yoneki, E., Behrens, N., et al. (2006) · Lecture notes in computer science · doi:10.1007/11915034_112 · Cited by 67 · see citing works
GroupUs: Smartphone Proximity Data and Human Interaction Type Mining
Tri, T. M., & Gática-Pérez, D. (2011) · International Symposium on Wearable Computers · doi:10.1109/iswc.2011.28 · Cited by 66 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Matcham, F., et al. (2021) · JMIR mHealth and uHealth · doi:10.2196/29840 · Cited by 68 · see citing works
- Authors
- Yan, Z., Yang, J., & Tapia, E. M.
- Affiliations
- Samsung Research America - Silicon Valley, San Jose, CA, USA
- ORCID
- Yan, Zhixian
- Venue
- Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
- Publisher
- ACM
- Published
- 2013-09-08
- Pages
- 95-98
- OpenAlex
- W2144615716
- Cited by
- 24
- References
- 5
- Citations by year
- 2024: 2 · 2023: 3 · 2021: 6 · 2020: 4 · 2018: 3 · 2017: 2 · 2016: 2 · 2015: 1 · 2014: 1
- FWCI
- 1.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Bluetooth and Wireless Communication Technologies
- Keywords
- Bluetooth, social sensing, people-centric sensing, smartphones, context inference, similarity features
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
