Lovén, L.; Lähderanta, T.; Ruha, L.; Peltonen, E.; Launonen, I.; Sillanpää, M.J.; Riekki, J.; Pirttikangas, S. EDISON: An Edge-Native Method and Architecture for Distributed Interpolation. Sensors 2021, 21, 2279. https://doi.org/10.3390/s21072279
EDISON : an edge-native method and architecture for distributed interpolation
|Author:||Lovén, Lauri1; Lähderanta, Tero2; Ruha, Leena2,3;|
1Center for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, Finland
2Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland
3Natural Resources Institute Finland, FI-90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202103319020
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2021-03-31
Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research is supported by Academy of Finland 6Genesis Flagship (grant 318927), the Infotech Oulu research institute, the Future Makers program of the Jane and Aatos Erkko Foundation and the Technology Industries of Finland Centennial Foundation, and the personal grant for Lauri Lovén on Edge-native AI research by the Tauno Tönning foundation. Further, this research has received funding from the ECSEL Joint Undertaking (JU) project “FRACTAL: A Cognitive Fractal and Secure edge based on a unique Open-Safe-Reliable-Low Power Hardware Platform” under grant agreement No 877056. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Italy, Austria, Germany, France, Finland, Switzerland.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.