INLA for Adaptive UAV Sampling of Radio Environment Maps in IoT Networks: Prior Width Governs Accuracy and Calibration
Read the original on arXiv Machine Learning →The paper investigates adaptive UAV sampling for constructing radio environment maps (REMs) from non‑Gaussian IoT observations using Integrated Nested Laplace Approximation (INLA) to update a Matérn Gaussian Markov random field posterior. Waypoints are chosen via entropy pre‑screening and a Sherman–Morrison lookahead, and the study finds that widening the prior shadowing standard deviation consistently improves NMSE and credible‑interval coverage across multiple ray‑traced windows, while online width estimation from UAV counts does not recover this gain. Comparative analyses show that posterior‑informed sampling outperforms random and lawnmower strategies, but differences among informative acquisition rules remain unresolved.
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