The term " Edge Artificial Intelligence (Edge AI)" refers to the part of a network where data is analysed and aggregated.
Dispersed networks, such as those found in the Internet of Things (IoT), have enormous ramifications when it comes to "Edge AI," or
"intelligence at the edge". Smartphone applications like real-time traffic data and facial recognition data, including semi-autonomous
smart devices and automobiles are integrated in this class. Edge AI products include wearable health monitors, security cameras, drones,
robots, smart speakers and video games. Edge AI was established due to the marriage of Artificial Intelligence with cutting Edge
Computing (EC) systems. Edge Intelligence (EI) is a terminology utilized to define the model learning or the inference processes, which
happen at the system edge by employing available computational resources and data from the edge nodes to the end devices under cloud
computing paradigm. This paper provides a light on "Edge AI" and the elements that contribute to it. In this paper, Edge AI's motivation,
definition, applications, and long-term prospects are examined.
Keywords
Artificial Intelligence (AI), Edge Artificial Intelligence (Edge AI), Edge Computing (EC), Internet of Things (IoT),
Machine Learning (ML).
R. Meneguette, R. De Grande, J. Ueyama, G. P. R. Filho, and E. Madeira, “Vehicular Edge Computing: Architecture, resource management,
security, and challenges,” ACM Comput. Surv., vol. 55, no. 1, pp. 1–46, 2023.
G. Fortino, M. Zhou, M. M. Hassan, M. Pathan, and S. Karnouskos, “Pushing Artificial Intelligence to the Edge: Emerging trends, issues and challenges,” Eng. Appl. Artif. Intell., vol. 103, no. 104298, p. 104298, 2021.
A. B. P. Samson, S. R. A. Chandra, and M. Manikant, “A deep neural network approach for the prediction of protein subcellular localization,” Neural Netw. World, vol. 31, no. 1, pp. 29–45, 2021.
X. Tang, Y. Liu, Z. Zeng, and B. Veeravalli, “Service cost effective and reliability aware job scheduling algorithm on cloud computing systems,”
IEEE trans. cloud comput., pp. 1–1, 2022.
Y. Li and Y. Hong, “Prediction of football match results based on edge computing and machine learning technology,” Int. j. mob. comput.
multimed. commun., vol. 13, no. 2, pp. 1–10, 2022.
V. D. A. Kumar, A. Kumar, R. S. Batth, M. Rashid, S. K. Gupta, and M. Raghuraman, “Efficient data transfer in edge envisioned environment
using artificial intelligence based edge node algorithm,” Trans. emerg. telecommun. technol., vol. 32, no. 6, 2021.
S. Liu, C. Guo, F. Al-Turjman, K. Muhammad, and V. H. C. de Albuquerque, “Reliability of response region: A novel mechanism in visual
tracking by edge computing for IIoT environments,” Mech. Syst. Signal Process., vol. 138, no. 106537, p. 106537, 2020.
Y. Chen, W. Tong, D. Feng, and Z. Wang, “Cora: Data correlations-based storage policies for cloud object storage,” Future Gener. Comput.
Syst., vol. 129, pp. 331–346, 2022.
“Presidential working group on artificial intelligence,” Ucop.edu. [Online]. Available: https://www.ucop.edu/ethics-compliance-audit-
services/compliance/presidential-working-group-on-artificial-intelligence.html. [Accessed: 08-Mar-2022].
M. Merenda, C. Porcaro, and D. Iero, “Edge machine learning for AI-enabled IoT devices: A review,” Sensors (Basel), vol. 20, no. 9, p. 2533,2020.
J. Sun, C. Yang, T. Tanjo, K. Sage, and K. Aida, “Implementation of self-adaptive middleware for mobile vehicle tracking applications on edge
computing,” in Internet and Distributed Computing Systems, Cham: Springer International Publishing, 2018, pp. 1–15.
D. Han, N. Pan, and K.-C. Li, “A traceable and revocable ciphertext-policy attribute-based encryption scheme based on privacy protection,”
IEEE Trans. Dependable Secure Comput., vol. 19, no. 1, pp. 316–327, 2022.
K. Taji, R. Ait Abdelouahid, I. Ezzahoui, and A. Marzak, “Review on architectures of aquaponic systems based on the Internet of Things and
artificial intelligence: Comparative study,” in The 4th International Conference on Networking, Information Systems amp Security, 2021.
H. Stewart and C. Aitken, “Prevent rather than respond: Predictive analytics for health and safety,” in Day 2 Wed, September 04, 2019, 2019.
dearC, “Northstar — The Latest & Greatest in Drag-and-drop data analytics from MIT and Brown University,” Towards Data Science, 04-Jul-
2019. [Online]. Available: https://towardsdatascience.com/northstar-the-latest-greatest-in-drag-and-drop-data-analytics-from-mit-and-brown-
university-4946dd1107cb?gi=43567d16327. [Accessed: 08-Mar-2022].
Haldorai, A. Ramu, and S. Murugan, “Signal Processing Architectures, Algorithms, and Human–Machine Interactions in Urban Applications,”
Computing and Communication Systems in Urban Development, pp. 49–67, 2019. doi:10.1007/978-3-030-26013-2_3
Haldorai, A. Ramu, and S. Murugan, “Artificial Intelligence and Machine Learning for Future Urban Development,” Computing and
Communication Systems in Urban Development, pp. 91–113, 2019. doi:10.1007/978-3-030-26013-2_5
Haldorai, A. Ramu, and S. Murugan, “Energy Efficient Network Selection for Urban Cognitive Spectrum Handovers,” Computing and
Communication Systems in Urban Development, pp. 115–139, 2019. doi:10.1007/978-3-030-26013-2_6
Haldorai, A. Ramu, and S. Murugan, “Social Relationship Ranking on the Smart Internet,” Computing and Communication Systems in Urban
Development, pp. 141–159, 2019. doi:10.1007/978-3-030-26013-2_7
Haldorai, A. Ramu, and S. Murugan, “Cognitive Radio Communication and Applications for Urban Spaces,” Computing and Communication
Systems in Urban Development, pp. 161–183, 2019. doi:10.1007/978-3-030-26013-2_8
Haldorai, A., Ramu, A., & Murugan, S. (2019). Machine Learning and Big Data for Smart Generation. Computing and Communication Systems
in Urban Development, 185–203. doi:10.1007/978-3-030-26013-2_9.
Haldorai, A. Ramu, and S. Murugan, “Smart Sensor Networking and Green Technologies in Urban Areas,” Computing and Communication
Systems in Urban Development, pp. 205–224, 2019. doi:10.1007/978-3-030-26013-2_10
G. Gokilakrishnan, S. Ganeshkumar, H. Anandakumar and M. Vigneshkumar, "A Critical Review of Production Distribution Planning Models,"
2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 2047-2051, doi:
10.1109/ICACCS51430.2021.9441879.
S. Murugan and A. Haldorai, “Role of Machine Intelligence and Big Data in Remote Sensing,” Advances in Data Mining and Database
Management, pp. 118–130, 2019.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
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Anandakumar Haldorai
Anandakumar Haldorai
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, India.
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Cite this article
Anandakumar Haldorai, Shrinand Anandakumar, “Motivation, Definition, Application and the Future of Edge Artificial Intelligence", vol.2, no.3, pp. 077-087, July 2022. doi: 10.53759/181X/JCNS202202011.