Optimizing FANET Routing With Predictive Link Reliability: A Delay-Constrained Approach

Abstract

Flying ad hoc networks (FANETs) operate in highly dynamic environments where ensuring reliable communication with minimal delay is a major challenge. In this paper, we present a novel approach to optimize FANET routing by integrating predictive models for link reliability with delay constraints. Conventional routing methods often overlook the fluctuating conditions and high mobility of FANETs, resulting in suboptimal performance and increased latency. Our proposed strategy utilizes historical data and machine learning techniques to predict the reliability of connections. This enables more informed routing decisions that account for potential link failures and delays in advance. By incorporating these predictive insights, our routing algorithm dynamically adapts to changing network conditions and optimizes path selection to ensure both reliability and timeliness of data transmission. Extensive simulations and real-world experiments show that our approach significantly reduces end-to-end delay and improves overall network performance compared to existing routing protocols. This research provides a robust framework for improving the efficiency and reliability of communications in delay-sensitive FANET applications and paves the way for more resilient and responsive air networks.