The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mobile crowdsensing: current state and future challenges
Abstract
An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.
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Cites
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Cited by
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
Edge-centric Computing: Vision and Challenges
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., & Riviere, E. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2831347.2831354 · Cited by 934
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
Opportunities in mobile crowd sensing
Ma, H., Zhao, D., & Yuan, P. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6871666 · Cited by 466
Resource Management in Fog/Edge Computing: A Survey on Architectures, Infrastructure, and Algorithms
Hong, C.-H., & Varghese, B. (2020) · ACM Computing Surveys · doi:10.1145/3326066 · Cited by 461
Metadata
- Authors
- Ganti, R., Ye, F., & Lei, H.
- Venue
- IEEE Communications Magazine
- Published
- 2011-11
- Volume
- 49
- Issue
- 11
- Pages
- 32-39
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2125826911
- Cited by
- 2,054
- References
- 1
- FWCI
- 98.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ganti, R., Ye, F., & Lei, H. (2011). Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2011.6069707
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
