Sensors, Volume 21, Issue 7 (April-1 2021) – 319 articles
Cover Story (view full-size image): In smart cities, spatio-temporal interpolation provides a fine-grained understanding of local phenomena, such as weather, air quality, or traffic data, offering estimates of observations in unobserved locations and time slots. However, with the ever-increasing sensor data, the data transmission and processing requirements of the predominantly centralized architectures have become unfeasible. To address this scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing the data and the computations between device, edge and cloud layers. The results show that EDISON provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation, and a 6% smaller RMSE than a baseline-distributed approach. View this paper.
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