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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Online Learning Framework for Radio Link Failure Prediction in FANETs

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DOI: http://dx.doi.org/10.15439/2023F8996

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 41–48 ()

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Abstract. 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.

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