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EDISON: An Edge-Native Method and Architecture for Distributed Interpolation.

Lauri Lovén1, Tero Lähderanta2, Leena Ruha2,3

  • 1Center for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, Finland.

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|April 3, 2021
PubMed
Summary
This summary is machine-generated.

EDISON offers a distributed edge-native architecture for spatio-temporal interpolation, improving scalability and accuracy. This approach enhances smart city data analysis by processing information closer to the source, reducing computational load.

Keywords:
distributed AIdistributed computingedge computingedgeAIinterpolationkriging

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Area of Science:

  • Computer Science
  • Data Science
  • Urban Computing

Background:

  • Spatio-temporal interpolation is crucial for understanding urban phenomena in smart cities.
  • Current centralized cloud-based approaches face scalability issues due to increasing data density.
  • Limitations include high transmission bandwidth and computational demands.

Purpose of the Study:

  • To address the scalability challenges in spatio-temporal interpolation for smart cities.
  • To propose a novel distributed learning and inference framework named EDISON.
  • To enable efficient distribution of models, computations, and data across device, edge, and cloud layers.

Main Methods:

  • Developed EDISON, an edge-native architecture for distributed spatio-temporal interpolation.
  • Implemented algorithms for distributed learning and inference.
  • Validated functionality in a simulated spatio-temporal setup with 1 million data points.

Main Results:

  • EDISON demonstrates improved performance over alternative interpolation methods.
  • Achieved up to 10% smaller Root Mean Square Error (RMSE) compared to global interpolation.
  • Showcased a 6% smaller RMSE than a baseline distributed approach.

Conclusions:

  • EDISON provides a scalable and efficient solution for spatio-temporal interpolation in data-intensive environments.
  • The distributed edge-native architecture effectively manages computational load and enhances accuracy.
  • This framework is well-suited for smart city applications requiring fine-grained spatio-temporal understanding.