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

Abstract

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.

Recent Trends on IoT Systems for Traffic Monitoring and for Autonomous and Connected Vehicles

“…The special issue is characterized by 11 original research papers [1][2][3][4][5][6][7][8][9][10][11]. The first paper [1], "Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction", is written by M. V. Peppa, T. Komar, Wen Xiao, P. James, C. Robson, Jin Xing, and S. Barr from the University of Newcastle, UK; and the University of Melbourne, Australia.…”

“…Paper [9], titled "PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction", is authored by P. Karvelis Meteorology has always had a key role in transportation and mobility systems. 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 special issue is characterized by 11 original research papers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

“…Paper [ 9 ], titled “PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction”, is authored by P. Karvelis, D. Mazzei, M. Biviano, C. Stylios, from the University of Ioannina and Computer Technology & Press Diophantus in Greece and from the University of Pisa in Italy.…”

Elastic AI: system support for adaptive machine learning in pervasive computing systems

“…First, we created a monolithic AI twin in the Cloud that takes five different environmental phenomena into account to perform a weather forecast: wind speed, wind direction, air pressure, temperature, and humidity. The AI model is taken from Karvelis et al (2020). For sake of simplicity we focus solely on the wind speed prediction.…”