Failure Prediction in Software Defined Flying Ad-hoc Network

This research aims to propose an approach to address the unpredictability topology state issue of FANET. The mobility of the network can lead to frequent link disruptions, causing communication unavailability. To mitigate this, our goal is to implement an AI algorithm that can identify patterns in UAV mobility, predict potential disconnections, and trigger rerouting/forwarding algorithms in advance. This paper presents an example of an SD-FANET able to provide wireless in-band telemetry to the AI-equipped edge node placed at the ground station, discusses the design of subsystems hosting the AI process, and demonstrates how a machine learning model can recognize critical network situations without relying on complex neural networks.