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2 Annals of Computer Science and Information Systems

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Online Learning Framework for Radio Link Failure Prediction in FANETs

Danilchenko Kiril, Lazmi Nir, Segal Michael

Annals of Computer Science and Information Systems

2023

Vol. 35

41--48

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.

An Efficient Connected Swarm Deployment via Deep Learning

Danilchenko Kiril, Segal Michael

Annals of Computer Science and Information Systems

2021

Vol. 25

1--7

In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied. It aims to maximize the sum of the weights of the ground users covered by UAVs while UAVs forming a connected communication graph. We focus on the case where the number of UAVs is not necessarily enough to cover all ground users. We develop an UAV Deployment Deep Neural network (mod) as a UAV's deployment deep network method. Simulation results demonstrate that mod can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and outperform the state-of-the-art traditional algorithms.