Study and Design of a data collection system based on IoT technology for the discovery and evaluation of renewable sources
Climate change and environmental pollution are global concerns impacting all living organisms, especially human health. Transitioning from fossil fuels to renewable energy sources like solar, wind, and geothermal power has gained importance in reducing greenhouse gas emissions. However, effectively assessing and exploiting renewable energy potential on land is challenging, necessitating data collection from various sources. The rise of smart cities has led to IoT-based data acquisition systems, enabling real-time data collection through environmental sensors across different locations, including public transportation.
Integrating IoT sensors on vehicles offers a seamless solution to gather environmental data and explore energy potentials. By equipping buses, cars, and trains with solar panels, wind speed sensors, and anemometers, we can assess renewable energy possibilities at various locations. Geothermal sources, being stationary, require remote sensing devices in remote areas, which can create coverage gaps due to lacking communication infrastructure.
To address this, we propose a system that uses devices on public transport to collect data on renewable energy sources and transmit it to a remote server or cloud. Employing machine learning techniques, we integrate and analyze this data alongside satellite data. Our research aims to explore the feasibility of IoT devices on vehicles to identify new solar, wind, and geothermal energy sources.