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Open AccessArticle

PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction

1
Department of Informatics and Telecommunication, University of Ioannina, 45110 Ioannina, Greece
2
Department of Computer Science, University of Pisa, 56126 Pisa, Italy
3
Computer Technology & Press “Diophantus”, 26504 Patras, Greece
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3181; https://doi.org/10.3390/s20113181
Received: 17 April 2020 / Revised: 29 May 2020 / Accepted: 30 May 2020 / Published: 3 June 2020
Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics. View Full-Text
Keywords: weather forecasting; microcontroller; IoT; maritime traffic weather forecasting; microcontroller; IoT; maritime traffic
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MDPI and ACS Style

Karvelis, P.; Mazzei, D.; Biviano, M.; Stylios, C. PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction. Sensors 2020, 20, 3181.

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