EDISON: An Edge-Native Method and Architecture for Distributed Interpolation
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)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/7/2279 |
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| author | Lauri Lovén Tero Lähderanta Leena Ruha Ella Peltonen Ilkka Launonen Mikko J. Sillanpää Jukka Riekki Susanna Pirttikangas |
| author_facet | Lauri Lovén Tero Lähderanta Leena Ruha Ella Peltonen Ilkka Launonen Mikko J. Sillanpää Jukka Riekki Susanna Pirttikangas |
| author_sort | Lauri Lovén |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:56:22Z |
| format | Article |
| id | doaj.art-a8d8c490a7b34f89be28c0b5e1090aba |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:56:22Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a8d8c490a7b34f89be28c0b5e1090aba2023-11-21T11:53:32ZengMDPI AGSensors1424-82202021-03-01217227910.3390/s21072279EDISON: An Edge-Native Method and Architecture for Distributed InterpolationLauri Lovén0Tero Lähderanta1Leena Ruha2Ella Peltonen3Ilkka Launonen4Mikko J. Sillanpää5Jukka Riekki6Susanna Pirttikangas7Center for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, FinlandResearch Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, FinlandResearch Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, FinlandCenter for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, FinlandResearch Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, FinlandResearch Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, FinlandCenter for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, FinlandCenter for Ubiquitous Computing, University of Oulu, FI-90014 Oulu, FinlandSpatio-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.https://www.mdpi.com/1424-8220/21/7/2279edgeAIedge computinginterpolationdistributed AIdistributed computingkriging |
| spellingShingle | Lauri Lovén Tero Lähderanta Leena Ruha Ella Peltonen Ilkka Launonen Mikko J. Sillanpää Jukka Riekki Susanna Pirttikangas EDISON: An Edge-Native Method and Architecture for Distributed Interpolation Sensors edgeAI edge computing interpolation distributed AI distributed computing kriging |
| title | EDISON: An Edge-Native Method and Architecture for Distributed Interpolation |
| title_full | EDISON: An Edge-Native Method and Architecture for Distributed Interpolation |
| title_fullStr | EDISON: An Edge-Native Method and Architecture for Distributed Interpolation |
| title_full_unstemmed | EDISON: An Edge-Native Method and Architecture for Distributed Interpolation |
| title_short | EDISON: An Edge-Native Method and Architecture for Distributed Interpolation |
| title_sort | edison an edge native method and architecture for distributed interpolation |
| topic | edgeAI edge computing interpolation distributed AI distributed computing kriging |
| url | https://www.mdpi.com/1424-8220/21/7/2279 |
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