Cited by 154IoT and Edge/Fog ComputingCloud Computing and Resource ManagementEnergy Efficiency in Computing

Fog computing: a taxonomy, systematic review, current trends and research challenges

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Singh, Jagdeep, Singh, Parminder, Gill, Sukhpal Singh·2021·Journal of Parallel and Distributed Computing

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Crossref · 10.1016/j.jpdc.2021.06.005

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219 of 220 references verified0 citation errors · 0 not checkable

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  • Moving resources closer to the fixed edge represented by surveillance cameras, traffic flow enhancement systems, and smart meters is a feature of fog computing.

    “Sectors: Moving resources closer to the”

    From Edge AI: A survey (2023) · cites this paper as [13]

    Section A. Context says fog computing places storage, computing, and networking functions near connected devices, supporting the claim’s central point. The paper also discusses video surveillance (Development) and fog-based traffic-signal control (Platforms), but the passages do not establish the claimed ‘fixed edge’ category or identify smart meters as an example. (LLM, conf=0.83)

    Experimental — accuracy not yet measured
  • Bodies such as ETSI have extended the use of the word 'mobile' to include immobile but rotatable devices such as surveillance cameras.

    “In addition, bodies such as ETSI have extended the use of the word “mobile” to include devices such as surveillance cameras which are immobile (but rotatable) [13].”

    From Edge AI: A survey (2023) · cites this paper as [13]

    The Expectation/Interest section discusses mobility in fog computing, and the Development section mentions cameras on vehicles, but neither defines stationary, rotatable cameras as “mobile.” Searches for ETSI, surveillance cameras, and immobile or rotatable devices found no passage addressing the attributed definition. The retrieved passages have no page numbers. (LLM, conf=0.76)

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Cites

Flagged first, then by citations

6 of the 226 references couldn’t be matched to a known paper.

Research works printed but not linked: 9

  1. R. Mahmud, R. Kotagiri, and R. Buyya, "Fog Com- puting: A Taxonomy, Survey and Future Directions," in Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives. Springer Singapore, 2018, pp. 103-130.
  2. R. Neware, "Fog Computing Architecture, Applications and Security Issues: A Survey," no. March 2019, 2019. [Online]. Available: www.preprints.org
  3. Y. Sankara Rao, K. Bhavya Sree, and A. Professor, "A Review on Fog Computing : Conceptual Live Vm Migration Framework, Issues, Applications and Its Challenges," International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT, vol. 1, no. 10, pp. 1175-1184, 2018. [Online]. Available: www.ijsrcseit.com
  4. S. Roy, N. Ghosh, P. Ghosh, and S. K. Das, "bioMCS: A Bio-inspired Collaborative Data Transfer Framework over Fog Computing Platforms in Mobile Crowdsens- ing," in ICDCN 2020: Proceedings of the 21st In- ternational Conference on Distributed Computing and Networking, no. January. ACM, 2020, pp. 1-10.
  5. Q. Deng, M. Goudarzi, and R. Buyya, "Fogbus2: A lightweight and distributed container-based framework for integration of iot-enabled systems with edge and cloud computing," SIGMOD 2021 International Work- shop on Big Data in Emergent Distributed Environments (BiDEDE 2021), June 20-25, 2021.
  6. Q. T. Minh, C. M. Tran, T. A. Le, B. T. Nguyen, T. M. Tran, and R. K. Balan, "Fogfly: A traffic light optimization solution based on fog computing," in Ubi- Comp/ISWC 2018 -Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers, 2018, pp. 1130-1139.
  7. M. Arif, G. Wang, V. E. Balas, O. Geman, A. Cas- tiglione, and J. Chen, "Sdn based communications privacy-preserving architecture for vanets using fog computing," Vehicular Communications, vol. 26, p. 100265, 2020.
  8. F. L. Lobo, M. Lima, H. Oliveira, J. Harrington, and K. El-Khatib, "SoLVE: A localization system frame- work for VANets using the cloud and fog comput- ing," in DIVANet 2017 -Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, Co-located with MSWiM 2017, 2017, pp. 17-22.
  9. Y. Verginadis, D. Apostolou, S. Taherizadeh, I. Ledakis, G. Mentzas, A. Tsagkaropoulos, N. Papageorgiou, and F. Paraskevopoulos, "Prestocloud: A novel framework for data-intensive multi-cloud, fog, and edge function- as-a-service applications," Information Resources Man- agement Journal (IRMJ), vol. 34, no. 1, pp. 66-85, 2021.

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Authors
Singh, J., Singh, P., & Gill, S. S.
Affiliations
  • Lovely Professional University (IN)
  • Queen Mary University of London (GB)
Corresponding author
Gill, Sukhpal Singh
Venue
Journal of Parallel and Distributed Computing
Publisher
Elsevier BV
Published
2021-11
Volume
157
Pages
56-85
ISSN
0743-7315 (ISSN-L)
OpenAlex
W3174273494
Cited by
154
References
226
Citations by year
2026: 14 · 2025: 26 · 2024: 28 · 2023: 46 · 2022: 33 · 2021: 7
FWCI
4.58
Type
journal article
Language
en
Open access
Locations
  • Journal of Parallel and Distributed Computing (publishedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
Funders
  • University of Texas at Austin
  • University of Melbourne
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
fog computing, taxonomy (general classification scheme), Internet of Things, quality of service, fog computing architecture, latency, scalability, privacy and security, communication modes

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