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Cuadrado, FelixAuthor

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October 29, 2024
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Edge AI: A Taxonomy, Systematic Review and Future Directions

Publicated to: Cluster Computing-The Journal of Networks Software Tools and Applications. 28 (1): 18- - 2025-02-01 28(1), DOI: 10.1007/s10586-024-04686-y

Authors:

Gill, SS; Golec, M; Hu, JM; Xu, MX; Du, JH; Wu, HM; Walia, GK; Murugesan, SS; Ali, B; Kumar, M; Ye, KJ; Verma, P; Kumar, S; Cuadrado, F; Uhlig, S
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Affiliations

Abdullah Gul Univ, Kayseri, Turkiye - Author
Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China - Author
Dr BR Ambedkar Natl Inst Technol, Dept Informat Technol, Jalandhar, India - Author
GLA Univ, Dept Comp Engn & Applicat, Mathura, India - Author
Natl Inst Technol, Dept Informat Technol, Srinagar, India - Author
Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England - Author
Tech Univ Madrid UPM, Madrid, Spain - Author
Tianjin Univ, Ctr Appl Math, Tianjin, Peoples R China - Author
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Abstract

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while also examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.
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Keywords

Adversarial machine learningAlgorithmsArchitectureArtificial intelligenceArtificial intelligence systemsArtificial intelligence technologiesCloudCloud computingCloud platformsCloud-computingData confidentialityData integrityEdge aEdge aiEdge artificial intelligenceEdge computingFuture research directionsInternetLessonMachine learningMachine-learningNeural-networkResource-allocationSecuritySystematic reviewTaxonomies

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Cluster Computing-The Journal of Networks Software Tools and Applications due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 31/147, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-05:

  • Google Scholar: 16
  • WoS: 54
  • Scopus: 78
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-05:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 192.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 192 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.
  • The number of mentions on the social network X (formerly Twitter): 4 (Altmetric).
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China; India; Turkey; United Kingdom.

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Awards linked to the item

Funding M. Golec is supported by the Ministry of Education of the Turkish Republic. B. Ali is supported by the Ph.D. Scholarship at the Queen Mary University of London. H. Wu is supported by the National Natural Science Foundation of China (No. 62071327) and Tianjin Science and Technology Planning Project (No. 22ZYYYJC00020). F. Cuadrado has been supported by the HE ACES project (Grant No. 101093126). M. Xu is supported by the National Natural Science Foundation of China (No. 62102408), Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515010251), Shenzhen Industrial Application Projects of undertaking the National key R & D Program of China (No. CJGJZD20210408091600002).
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