Experiences from measuring human mobility using Bluetooth inquiring devices
Abstract
We present an analysis of human mobility measurements using Bluetooth devices. A number of data traces from such measurements have been made publicly available for the benefit of the research community. However, the limitations of the measurement approaches are in general not well known. We have been given access to the Intel Motes and to the software that was used to perform some of the previous measurements. We present a detailed description of this platform, an analysis of the software used, and provide information on the improvements we have made to remove a number of limitations in the software. To illustrate the impact of our changes, we provide results from two new data collection efforts and compare them to the previous experiments. One of our major results is that our new software makes significant improvements in the number of contacts that can be sampled during each Bluetooth scan. We discuss the impact of these findings.
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Continuously recorded NBDC data represents a mixed signal that has been used to estimate individuals' behaviors and statuses, including face-to-face social interactions, working status, mobility, and isolation and loneliness.
“The continuously recorded NBDC data represents a mixed signal that has been used to estimate individuals’ behaviors and statuses, including face-to-face social interactions [8-10], working status [11], mobility [12], and isolation and loneliness [13,14].”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [12]
Cites
Impact of Human Mobility on Opportunistic Forwarding Algorithms
Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., & Scott, J. (2007) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2007.1060 · Cited by 1,024 · see citing works
Self-citationPocket switched networks and human mobility in conference environments
Hui, P., Chaintreau, A., SCOTT, J. R., et al. (2005) · doi:10.1145/1080139.1080142 · Cited by 951 · see citing works
Impact of Human Mobility on the Design of Opportunistic Forwarding Algorithms
Chaintreau, A., Hui, P., Crowcroft, J., et al. (2006) · doi:10.1109/infocom.2006.172 · Cited by 588 · see citing works
Opportunistic content distribution in an urban setting
Leguay, J., Lindgren, A., SCOTT, J. R., et al. (2006) · doi:10.1145/1162654.1162657 · Cited by 191 · see citing works
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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
- Nordström, E., Diot, C., Gass, R., & Gunningberg, P.
- Affiliations
- Technicolor (France) (FR)
- Intel (United States) (US)
- Uppsala University (SE)
- Venue
- Proceedings of the 1st international workshop on System evaluation for mobile platforms
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 15-20
- OpenAlex
- W2155948997
- Cited by
- 25
- References
- 4
- Citations by year
- 2021: 1 · 2019: 1 · 2016: 2 · 2012: 4
- FWCI
- 3.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Bluetooth and Wireless Communication Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Opportunistic and Delay-Tolerant Networks
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- Bluetooth, mobility data collection
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