Towards EDISON: An Edge-Native Approach to Distributed Interpolation of Environmental Data

Prevalent weather prediction methods are based on sensor data, collected by satellites and a sparse grid of stationary weather stations. Various initiatives improve the prediction models by including additional data sources such as mobile weather sensors, mobile phones, and wireless sensor networks (WSN) of, for example, smart homes. The underlying computing paradigm is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable. When the spatial and temporal density of weather sensor data grows, the required data transmission capacities and computational resources become unfeasible. We identify the challenges posed by spatial distribution of a weather prediction model, and suggest solutions for those challenges. We propose EDISON: an edge-native interpolation approach based on AI methods, distributed horizontally on edge servers. Finally, we demonstrate EDISON with a simple, simulated setup.