Calibrating Attribution Proxies for Reward Allocation in Participatory Weather Sensing
This work addresses the open problem of data valuation for incentive mechanisms in large-scale IoT weather sensing networks, offering a computationally feasible alternative to adjoint-based methods.
The paper proposes using gradient-based attribution from differentiable AI weather models to value individual data contributions in participatory weather sensing networks, evaluating over 400 configurations. It finds that attribution captures near-optimal sensor placement utility with monotonically faithful payments but is vulnerable to adversarial inflation, requiring external baseline data for detection.
Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contributions bring to the network remains an open problem. Existing approaches address data quality but not data valuation; in operational meteorology, adjoint-based methods derive value from the forecast model itself but require full data assimilation infrastructure. We propose to utilise differentiable AI weather models to fill this gap and characterise gradient-based attribution on gridded GFS analysis inputs as a candidate value signal, evaluating fidelity, calibration, cost, and gaming vulnerability across more than 400 configurations. Attribution captures near-optimal sensor placement utility with monotonically faithful payments, but can be inflated by adversarial inputs, with detection requiring external baseline data. These findings establish gradient attribution as a computationally validated signal for model-informed reward allocation in participatory weather sensing.