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Cited by 203IoT and Edge/Fog ComputingInternet of Things and AICloud Computing and Resource Management

The DOI matches its Crossref record; 485 of the 494 references it cites were found in a registry or an index.

Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions

Ahmad, Shahnawaz, Shakeel, Iman, Mehfuz, Shabana, Ahmad, Javed·2023·Computer Science Review
DOI 10.1016/j.cosrev.2023.100568
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Crossref · 10.1016/j.cosrev.2023.100568

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485 of 485 references verified0 citation errors · 0 not checkable

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Citations

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Show all 494 references

Research works printed but not linked: 28

  1. Sahu, 2018, Soft computing approach for prediction of software reliability, ICIC Express Lett., 12, 1213
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  3. Mohamed, 2014, Acoustic modeling using deep belief networks, IEEE Trans. Audio Speech Lang. Process., 20, 14, 10.1109/TASL.2011.2109382 · DOI
  4. Min, 2020, Public welfare organization management system based on FPGA and deep learning, Microprocess. Microsyst., 80
  5. Xu, 2018, The history and application of cloud computing, Inf. Rec. Mater., 19, 6667
  6. Zha, 2018, Edge computing: Platforms; Applications and challenges, J. Comput. Res. Dev., 55, 327
  7. Khan, 2019, Edge computing: A survey, Future Gener. Comput. Syst., 97, 219, 10.1016/j.future.2019.02.050 · DOI · Paper page
  8. Mao, 2017, A survey on mobile edge computing: The communication perspective, IEEE Commun. Surv. Tutor., 19, 2322, 10.1109/COMST.2017.2745201 · DOI · Paper page
  9. Zhu, 2017, Mobile network edge computing and caching technology, Railw. Comput. Appl., 26, 51
  10. Roman, 2018, Mobile edge computing: A survey and analysis of security threats and challenges, Future Gener. Comput. Syst., 78, 680, 10.1016/j.future.2016.11.009 · DOI · Paper page
  11. Hu, 2017, Survey on fog computing: architecture, key technologies, applications, and open issues, J. Netw. Comput. Appl., 98, 27, 10.1016/j.jnca.2017.09.002 · DOI
  12. Wang, 2017, A survey on mobile edge networks: Convergence of computing, caching, and communications, IEEE Access, 5, 6757, 10.1109/ACCESS.2017.2685434 · DOI · Paper page
  13. Roman, 2016, Mobile edge computing, fog et al.: a survey and analysis of security threats and challenges, Future Gener. Comput. Syst., 78
  14. Armbrust, 2013, A view of cloud computing, Int. J. Comput. Technol., 4, 50
  15. S. Yi, Z. Hao, Z. Qin, Q. Li, Fog computing: Platform and applications, in: Proceedings of the 3rd Workshopon Hot Topics in Web Systems and Technologies, HotWeb 2015, Washington, DC, USA, 24–25 October 2016, pp. 73–78.
  16. Mukherjee, 2018, Survey of fog computing: Fundamental, network applications, and research challenges, IEEE Commun. Surv. Tutor., PP
  17. K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder, B. Koldehofe, Mobile Fog: A ProgrammingModel for Large–Scale Applications on the Internet of Things, in: Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing, Hong Kong, China, 16 August 2003, p. 15. · DOI · Paper page
  18. Khan, 2017, Fog computing security: A review of current applications and securitysolutions, J. Cloud Comput., 6, 19, 10.1186/s13677-017-0090-3 · DOI · Paper page
  19. Alrawais, 2017, Fog computing for the internet of things: Security and privacy issues, IEEE Internet Comput., 21, 34, 10.1109/MIC.2017.37 · DOI
  20. Peter, 2015, FOG computing and its real-time applications, Int. J. Emerg. Technol. Adv. Eng., 5, 266
  21. Ali, 2019, Comparison of fog computing & cloud computing, Int. J. Math. Sci. Comput., 31
  22. Ni, 2017, Securing fog computing for the internet of things applications: challenges and solutions, IEEE Commun. Surv. Tutor., 20, 601, 10.1109/COMST.2017.2762345 · DOI · Paper page
  23. Hu, 2017, Security and privacy preservation scheme of face identification and resolution framework using fog computing in the internet of things, IEEE Internet Things J., 4, 1143, 10.1109/JIOT.2017.2659783 · DOI
  24. Xiong, 2018, Large-scale image-based fog detection based on cloud platform, Multimedia Tools Appl.
  25. Fu, 2018, Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing, IEEE Trans. Ind. Inform., 10.1109/TII.2018.2793350 · DOI
  26. Kuutti, 2020, A survey of deep learning applications to autonomous vehicle control, IEEE Trans. Intell. Transp. Syst., 22, 712, 10.1109/TITS.2019.2962338 · DOI · Paper page
  27. Attaallah, 2022, Analyzing the big data security through a unified decision-making approach, Intell. Autom. Soft Comput., 32, 1071, 10.32604/iasc.2022.022569 · DOI
  28. Chen, 2016, Deep features learning for medical image analysis with convolutional autoencoder neural network, IEEE Trans. Big Data

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Metadata

Authors
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J.
Affiliations
  • Jamia Hamdard (IN)
  • Jamia Millia Islamia (IN)
Corresponding author
Ahmad, Shahnawaz
ORCID
Venue
Computer Science Review
Publisher
Elsevier BV
Published
2023-08
Volume
49
Pages
100568
ISSN
1574-0137 (ISSN-L)
DOI
10.1016/j.cosrev.2023.100568
OpenAlex
W4378190897
Cited by
197
References
494
Citations by year
2026: 45 · 2025: 73 · 2024: 67 · 2023: 12
FWCI
20.18
Type
journal article
Language
en
Open access
closed
Licence
Locations
Computer Science Review (publishedVersion)
Funders
Jamia Millia Islamia
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
deep learning, cloud computing, edge computing, fog computing, Internet of Things, latency reduction, energy consumption, resource management, privacy, open problems

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