<ul><li>This work focuses on self-supervised placement-aware representation learning for multi-node IoT systems with spatially-distributed sensor observations.</li><li>The objective is to capture and distill spatial phenomena from distributed sensor observations across multiple vantage points.</li><li>The framework developed advances self-supervised model pretraining by encoding the relationship between sensor signals and observer vantage points, considering the spatial nature of IoT data.</li><li>Experiments on real-world datasets show the method's superior generalizability and robustness across various modalities, sensor placements, and application-level tasks.</li></ul>

SPAR: Self-supervised Placement-Aware Representation Learning for Multi-Node IoT Systems

Discover more