Resilient UAV Data Mule via Adaptive Sensor Association under Timing Constraints

TL;DR

This paper presents HGAD, an adaptive UAV data collection strategy that intelligently hovers over sensors based on real-time signal quality and buffer status, validated through digital twin and real-world experiments, outperforming traditional methods.

Contribution

Introduces HGAD, a novel adaptive hover-based strategy for UAV data collection that considers real-world signal conditions and sensor buffers, validated on digital twin and real testbeds.

Findings

HGAD improves data download stability and efficiency.

HGAD outperforms traditional strongest-signal following approaches.

Real-world experiments confirm the effectiveness of SNR-aware scheduling.

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.