The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Fog Computing: A Taxonomy, Survey and Future Directions
Open accessAbstract
In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
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
Mobile fog: a programming model for large-scale applications on the internet of things
Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., & Koldehofe, B. (2013) · Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing · doi:10.1145/2491266.2491270 · Cited by 486
Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT
Aazam, M., & Huh, E.-N. (2015) · 2015 IEEE 29th International Conference on Advanced Information Networking and Applications · doi:10.1109/aina.2015.254 · Cited by 365 · cited 2×
“In some papers based on the physical size, Fog servers are termed as micro servers, micro datacentres (Lee et al. [16], Aazam et al. [17]), nano servers (Jalali et al. [18]), etc. whereas other works categorize Fog servers based on their functionalities like cache servers (Zhu et al. [19]),…”
BackgroundFog computing architecture to enable consumer centric internet of things services
Datta, S. K., Bonnet, C., & Haerri, J. (2015) · 2015 International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2015.7177778 · Cited by 172
Cited by
All one needs to know about fog computing and related edge computing paradigms: A complete survey
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.009 · Cited by 1,368
Applications of Blockchains in the Internet of Things: A Comprehensive Survey
Ali, M. S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., & Rehmani, M. H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2886932 · Cited by 855
Fog Computing and the Internet of Things: A Review
Atlam, H., Walters, R., & Wills, G. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2020010 · Cited by 608
Security and privacy in fog computing: Challenges
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017) · IEEE Access · doi:10.1109/access.2017.2749422 · Cited by 533
Du, J., Zhao, L., Feng, J., & Chu, X. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2017.2787700 · Cited by 508
Metadata
- Authors
- Mahmud, R., Kotagiri, R., & Buyya, R.
- Venue
- Internet of Things
- Publisher
- Springer Singapore
- Published
- 2017-10-17
- Pages
- 103-130
- ISSN
- 2199-1073 (ISSN-L)
- 2199-1081
- OpenAlex
- W2549687423
- Cited by
- 921
- References
- 6
- FWCI
- 228.35
- Type
- book chapter
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fog Computing: A Taxonomy, Survey and Future Directions. https://app.cochat.ai/papers/10.1007/978-981-10-5861-5_5
- The paper
- Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog Computing: A Taxonomy, Survey and Future Directions. Internet of Things. https://doi.org/10.1007/978-981-10-5861-5_5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
