Resilient UAV Data Mule via Adaptive Sensor Association under Timing Constraints

TL;DR

This paper tackles time-constrained data collection by a UAV acting as a data mule from geographically distributed sensors under realistic wireless conditions. It introduces HGAD, a buffer- and SNR-aware hover-based strategy that optimizes sensor association and hovering to maximize data download within mission limits, and it compares against a baseline Greedy approach. The framework is validated across three evaluation modalities—digital twin, simulation, and a real-world AERPAW testbed—demonstrating that HGAD yields higher total data throughput and more stable downloads, with notable improvements in both fixed and autonomous trajectories. The work advances practical UAV data-mule design by integrating buffer states, SNR dynamics, and real-world trace data, with clear implications for time-sensitive and mission-critical deployments.

Abstract

Unmanned aerial vehicles (UAVs) can be critical for time-sensitive data collection missions, yet existing research often relies on simulations that fail to capture real-world complexities. Many studies assume ideal wireless conditions or focus only on path planning, neglecting the challenge of making real-time decisions in dynamic environments. To bridge this gap, we address the problem of adaptive sensor selection for a data-gathering UAV, considering both the buffered data at each sensor and realistic propagation conditions. We introduce the Hover-based Greedy Adaptive Download (HGAD) strategy, designed to maximize data transfer by intelligently hovering over sensors during periods of peak signal quality. We validate HGAD using both a digital twin (DT) and a real-world (RW) testbed at the NSF-funded AERPAW platform. Our experiments show that HGAD significantly improves download stability and successfully meets per-sensor data targets. When compared with the traditional Greedy approach that simply follows the strongest signal, HGAD is shown to outperform in the cumulative data download. This work demonstrates the importance of integrating signal-to-noise ratio (SNR)-aware and buffer-aware scheduling with DT and RW signal traces to design resilient UAV data-mule strategies for realistic deployments.

Figures (8)

• Figure 1: Representative UAV flight trajectory and sensor positions.
• Figure 2: UAV as a data mule flight trajectory: (a) fixed trajectory from DT and (b) autonomous trajectory from simulation.
• Figure 3: Experiment setup: (a) AERPAW UAV with SDR portable node, (b) UAV with USRP B205mini equipped portable node on the ground before flying, (c) UAV flying near a BS in the AERPAW testbed.
• Figure 4: Distribution of SNR values across four BSs based on (a) fixed trajectory in DT (Fig. \ref{['fig:trajectory']}(a)), (b) autonomous trajectory in simulation (Fig. \ref{['fig:trajectory']}(b)), (c) AERPAW field testbed-Flight 1, and (d) AERPAW field testbed-Flight 2.
• Figure 5: UAV fixed trajectory flight missions in AERPAW testbed: (a) Flight 1 and (b) Flight 2.