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Resiliants: WATER aims to create a predictive dashboard for avocado...
Our innovation uses wireless sensor nodes with Machine Learning to remotely measure Greenhouse Gases and pollutants by analyzing audio noise and environmental data. It's cost-effective, energy-efficient, and ideal for high-traffic areas like ports.
- Control the emission of GHG and air pollutants
- Regulate vehicular and port traffic in port areas
- Safeguard port personnel, ship passengers, and citizens in general by monitoring air quality in port areas
This innovation addresses multi-hazards and greenhouse effects.
See more information about this level and the TRL and SRL levels.
The proposed innovation relates to the Case Study #2 – Mediterranean Ports: Piraeus, Greece since it aims at monitoring and forecasting GHG and air pollutants concentration in the air, thus monitoring air quality within port areas. In this scenario, the health of personnel, passengers and citizens will be safeguarded, thus enhancing the quality of life. The innovation tackles the problem from a technical and a digital point of view, since innovative prototypes and embedded ML models will be designed and developed, whose scope is to set up a pervasive wireless measuring infrastructure to monitor and predict GHG emissions on the basis of the amount of both the vehicular and port traffic levels.
The innovation was developed independently as a research activity within the Electronic Measurements research group of the Department of Information Engineering of the University of Padova, Italy.
The crucial phase affecting the effectiveness of the proposed innovation lies in the data gathering process aimed at populating a dataset. Indeed, as any other data-driven method, the dataset on which the ML models are trained and validated must be as representative as possible of the problem to be solved. Therefore, such a phase will be planned in order to last for a sufficient timespan in which all of the possible scenarios to be forecasted will take place.
Our innovation uses wireless sensor nodes with Machine Learning to remotely measure Greenhouse Gases and pollutants by analyzing audio noise and environmental data. It's cost-effective, energy-efficient, and ideal for high-traffic areas like ports.
- Control the emission of GHG and air pollutants
- Regulate vehicular and port traffic in port areas
- Safeguard port personnel, ship passengers, and citizens in general by monitoring air quality in port areas
This innovation addresses multi-hazards and greenhouse effects.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
The proposed innovation relates to the Case Study #2 – Mediterranean Ports: Piraeus, Greece since it aims at monitoring and forecasting GHG and air pollutants concentration in the air, thus monitoring air quality within port areas. In this scenario, the health of personnel, passengers and citizens will be safeguarded, thus enhancing the quality of life. The innovation tackles the problem from a technical and a digital point of view, since innovative prototypes and embedded ML models will be designed and developed, whose scope is to set up a pervasive wireless measuring infrastructure to monitor and predict GHG emissions on the basis of the amount of both the vehicular and port traffic levels.
The innovation was developed independently as a research activity within the Electronic Measurements research group of the Department of Information Engineering of the University of Padova, Italy.
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