Journal of Computing and Natural Science

Open Source Network Optimization Tools for Edge Intelligence

Journal of Computing and Natural Science

Received On : 08 December 2021

Revised On : 20 March 2022

Accepted On : 25 March 2022

Published On : 05 April 2022

Volume 02, Issue 02

Pages : 055-065


It is indeed possible to bring analysis and information storage closer to where the information is generated by implementing an edge computing model. Response times should improve while bandwidth use is reduced as a result." A common misconception is that "edge" and "IoT" are synonymous. Using edge computing in the Internet of Things (IoT) is an example of this type of distributed computing, which is sensitive to configuration and location." Instead, then alluding to a specific piece of technology, the word refers to an overall architecture. In order to discover novel study opportunities and aid users in selecting more suitable edge computing advancements, this paper provides an analysis of existing open-source computing projects. Also, a comparison of the project’s applicability will be defined.


Edge Computing, Internet of Things, Machine Learning, Artificial Intelligence.

  1. [Online]. Available: [Accessed: 05-Mar-2022].
  2. [Online]. Available: [Accessed: 05-Mar-2022].
  3. W. Liang, Y. Ma, W. Xu, Z. Xu, X. Jia, and W. Zhou, “Request reliability augmentation with service function chain requirements in mobile edge computing,” IEEE Trans. Mob. Comput., pp. 1–1, 2021.
  4. “Worldwide spending on edge computing will reach $250 billion in 2024, according to a new IDC spending guide,” IDC: The premier global market intelligence company. [Online]. Available: [Accessed: 05-Mar-2022].
  5. O. Ali and M. K. Ishak, “Bringing intelligence to IoT Edge: Machine Learning based Smart City Image Classification using Microsoft Azure IoT and Custom Vision,” J. Phys. Conf. Ser., vol. 1529, no. 4, p. 042076, 2020.
  6. L. Peterson et al., “Central office re-architected as a data center,” IEEE Commun. Mag., vol. 54, no. 10, pp. 96–101, 2016.
  7. J. John, A. Ghosal, T. Margaria, and D. Pesch, “DSLs for model driven development of secure interoperable automation systems with EdgeX foundry,” in 2021 Forum on specification & Design Languages (FDL), 2021.
  8. S.-H. Lee, T. Yang, T.-S. Kim, and S. Park, “TTGN: Two-tier geographical networking for industrial internet of things with edge-based cognitive computing,” IEEE Access, vol. 10, pp. 22238–22246, 2022.
  9. L. Yang, “Data acquisition and transmission of laboratory local area network based on fuzzy DEMATEL algorithm,” Wirel. netw., 2021.
  10. K. Venkatachalam, P. Prabu, A. S. Alluhaidan, S. Hubálovský, and P. Trojovský, “Deep belief neural network for 5G diabetes monitoring in big data on edge IoT,” Mob. Netw. Appl., 2022.


The authors would like to thank to the reviewers for nice comments on the manuscript.


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Cite this article

Gregory Wang and David Steeg, “Open Source Network Optimization Tools for Edge Intelligence", vol.2, no.2, pp. 055-065, April 2022. doi: 10.53759/181X/JCNS202202009.


© 2022 Gregory Wang and David Steeg. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.