Generalizability of Dissimilarity Map Models

Background: Dynamic sensor positioning for spatial forecasting, such as solar irradiance prediction, requires strategies that balance coverage with the need for data in regions with high predictive uncertainty.

Question / Future Work: The current framework, while effective, relies on a dissimilarity map derived from a specific kriging model for guiding mobile sensors. A crucial next step is investigating the generalizability of the persistent coverage control algorithm when paired with other spatial interpolation or machine learning models used for generating the dissimilarity map.

Why It Matters: The dependence on a specific kriging model limits the broad applicability of the proposed control scheme; testing against alternative uncertainty quantification methods (like Gaussian Processes with different kernels or other spatial ML models) is necessary to validate its general robustness.

Evidence: future work on the robustness and applicability of the proposed persistent coverage control algorithm with various spatial interpolation or machine learning models for uncertainty quantification.