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Leveraging Lightweight Machine Learning for RF Jamming Detection in Mobile ad-hoc Networks: A Three-Tier Edge-Fog-Cloud Computing Approach Cover

Leveraging Lightweight Machine Learning for RF Jamming Detection in Mobile ad-hoc Networks: A Three-Tier Edge-Fog-Cloud Computing Approach

Cybernetics and Information Technologies
Volume 26 (2026): Issue 1 (March 2026)
By: Hakim Bessouf,  Abderrahmane Baadache and  Fouzi Semchedine  
Open Access
|Mar 2026

Authors

Hakim Bessouf

hakim.bessouf@univ-bejaia.dz

Research Unit LaMOS, Faculty of Exact Sciences, Department of Computer Science, University of Bejaia, Bejaia, Algeria

Abderrahmane Baadache

a.baadache@univ-alger.dz

Research Unit LaMOS, University of Bejaia, 06000, Algeria, Department of Computer Science, University of Algiers, Algiers, Algeria

Fouzi Semchedine

fouzi.semcheddine@univ-setif.dz

Department of Computer Science, University of Setif, Algeria
DOI: https://doi.org/10.2478/cait-2026-0005 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
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Language: English
Page range: 72 - 92
Submitted on: Jan 7, 2026
|
Accepted on: Feb 22, 2026
|
Published on: Mar 21, 2026
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Mobile Ad-Hoc Networks (MANETs),
RF jamming detection,
Lightweight machine learning,
Edge-fog-cloud computing,
Dynamic adaptation
Related subjects:
Computer sciences,
Information technology

© 2026 Hakim Bessouf, Abderrahmane Baadache, Fouzi Semchedine, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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