Dynamic Optimization in UAV-based Ad Hoc Networks (FANETs) for Real-Time Mission Routing using Quantum Walk Search Algorithms

Abstract

Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) are increasingly deployed in mission-critical applications such as disaster response and autonomous surveillance. However, traditional routing protocols often fail to maintain reliable and low-latency communication under the highly dynamic and probabilistic nature of FANET topologies, resulting in degraded performance in real-time scenarios. This study aims to develop a dynamic routing framework for FANETs that leverages Discrete-Time Quantum Walk (DTQW) algorithms to optimize mission paths in probabilistic graph-based network models. The network is modeled as a time-varying probabilistic graph , where link reliability is continuously updated based on UAV mobility and signal metrics. A quantum optimizer evaluates routing paths in superposition, exploiting parallelism to identify high-probability routes efficiently. This optimizer is integrated with the FANET routing layer to adaptively select optimal paths in real-time. Simulation results demonstrate that the proposed QW-Routing protocol significantly outperforms classical protocols such as AODV, DSR, and Random Walk Routing. Specifically, QW-Routing achieves a packet delivery ratio of 91.3%, reduces end-to-end latency by 22.4%, lowers energy consumption by 15%, and improves path stability by 39% compared to DSR under high-mobility conditions. The proposed framework introduces a scalable, low-overhead, and highly adaptive routing solution for FANETs, with direct applicability in next-generation autonomous aerial networks, where robust communication under uncertainty is paramount.