Dual-Link Data Resilient Edge-to-cloud Communication Framework for Agricultural Robots

Reliable and high-throughput communication between field robots and cloud services remain a key challenge in precision agriculture, where remote rural areas often lack consistent high-bandwidth connectivity. In this work, we introduce a new dual-link edge-to-cloud data transfer framework that combines long-range Low-Power Wide Area Networking (LPWAN) for essential control and monitoring with IEEE 802.11 Wi-Fi that carries bulk data over a Zenoh protocol. In addition, a data router dynamically switches the robot between ‘Transfer Mode’, in which sensor streams and imagery data are being forwarded via Wi-Fi, and ‘Storage Mode’, in which data are locally recorded in Robotic Operating System (ROS) 2 bags to prevent loss when connectivity degrades. To preemptively detect Wi-Fi link failures and issue routing instructions to the data router, an onboard anomaly detection node monitors heartbeat timing using a machine learning-based algorithm, namely the XGBoost model. Field trials demonstrate that (1) Wi-Fi transfers maintain sub-100 ms latency within 240 m of the gateway, (2) Long Range (LoRa) communication persists reliably beyond 350 m with ≈0.1 s latency, (3) the router achieves an average of 0.8 s overlap when entering Storage Mode, and (4) the anomaly detector successfully flags link degradation ahead of an outage. Our framework scales to multi-robot deployments via ROS 2 namespaces and Zenoh multicast, laying the groundwork for resilient swarm operations in rural environments.