Masked Autoencoders are Robust Task Offloaders for Timely and Accurate Inference

Abstract

Edge devices for robotics in hazardous environments, such as rescue drones, navigate complex terrains while transmitting images to remote servers for anomaly detection, including wildfires. However, these devices operate under strict resource constraints, prioritizing operational-critical tasks (e.g., autonomous navigation) while handling image-processing workloads with minimal overhead. Offloading computation to a remote server can alleviate this burden, but unstable network conditions can degrade accuracy and timeliness. To address these challenges, this paper presents a novel offloading framework that balances computational efficiency and accuracy in image-processing tasks. Specifically, it ensures (R1) a minimum accuracy level for individual image-processing tasks associated with different camera sensors and (R2) maximizes the overall image-processing accuracy across all sensors. Our approach builds on an edge-server collaborative image reconstruction architecture, where images are divided into patches and selectively reconstructed. To achieve R1 and R2, we introduce: (i) a hierarchical scheduler that effectively prioritizes patch transmissions under resource constraints and (ii) a feedback mechanism that adapts to network instability, ensuring reliable offloading and inference. Experimental results demonstrate that our framework maintains high accuracy and timely processing, even under network failures.