用AI模型梯度分析数据价值，实现气象传感激励分配。
Calibrating Attribution Proxies for Reward Allocation in Participatory Weather Sensing
- 用可微分AI模型计算数据梯度，作为贡献估值信号。
- 梯度估值近似最优传感器位置效用，支付与贡献单调匹配。
- 对抗样本会虚增估值，需外部基准数据检测风险。
大规模物联网气象感知网络需要激励机制以维持参与，但如何量化个体数据对网络的价值仍是一个开放问题。现有方法关注数据质量而非价值评估；在实际气象预报中，伴随方法虽能从预报模型推导价值，却依赖完整的数据同化基础设施。本文提出利用可微分的AI气象模型填补这一空白，将格点化GFS分析输入的梯度归因作为候选价值信号，并在400多个配置下评估其保真度、校准性、成本及抗操纵性。结果表明，梯度归因能准确捕捉接近最优的传感器部署效用，且支付与贡献呈单调关系，但可能被对抗性输入夸大，需借助外部基准数据进行检测。这些发现确立了梯度归因为模型驱动型奖励分配的计算验证信号。
原文摘要 · Abstract (English)
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.
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