Online Learning Framework for Radio Link Failure Prediction in FANETs

• Autorzy: Danilchenko Kiril , Lazmi Nir , Segal Michael

Identyfikatory

DOI

10.15439/2023F8996

• Języki publikacji: EN

Abstrakty

EN

In this paper, we consider the problem of prediction of Radio Link Failures (RLF) in flying ad hoc networks (FANETs). Many environmental factors that influence the quality of radio wave propagation are dynamic, and thus, drones must continually learn and update their radio link quality prediction model while they operate online. Online machine learning algorithms can be used to build adaptive RLF predictors without requiring a pre-deployment effort. To predict the RLF, we use an online machine learning algorithm and information gathering by message-passing from the neighbors. We propose an algorithm called ML-Net (Machine Learning and Network algorithm) to predict RLF. To the best of our knowledge, the combination of online machine learning algorithms together with the message-passing algorithm has not been used before. The proposed methodology outperforms the state-of-the-art online machine learning algorithms.

Słowa kluczowe

EN

online learning; RLF prediction; UAV

PL

nauka online; przewidywanie RLF; UAV

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 41--48
• Opis fizyczny: Bibliogr. 15 poz., tab., wykr., il.

Twórcy

autor

Danilchenko Kiril

• Department of Electrical and Computer Engineering University of Waterloo Waterloo, ON, Canada

autor

Lazmi Nir

• School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva, Israe

autor

Segal Michael

• School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva, Israe

Bibliografia

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Uwagi

1. Main Track Invited Contributions

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).