Environment observations provide a unique source of consistent information about the natural environment and they provide resource managers the information to assess the current state of the environment, weight the requirements of different uses by multiple stakeholders, and manage the natural resources and ecosystems in a sustainable manner. Most of the observations are based on satellites, but remote-sensing technologies alone cannot guarantee observations at the spatiotemporal resolution and with the accuracy requested for monitoring and modeling applications targeting, like weather and climate extremes and the complex feedback processes between the natural environment and human activities. Dense networks of standard and in-situ weather related sensors are present in EU and US, but it may happen that their data are not always available in real-time or updated with the required scale for various weather and climate applications. Then, high-resolution, (near) real-time on field monitoring systems are needed to satisfy the demand to sample environmental data, both in dense populated regions and in less developed and getting more populated regions, where essential in-situ observational capabilities can be lacking or deteriorating. The paper would demonstrate the possibility to have energy efficient computing and communication systems that can be employed for environment observation and that can enrich traditional in-situ and remote sensing environmental data, to enable a significant step forward in the environment monitoring of a wide range of weather and climate data. The paper will present an approach going in this direction (computing/communication everywhere with low-power constrains), tested in a harsh environment, by exploiting low-power boards to perform data pre-processing and reconfigurable antennas to send data in a more energetically convenient way applied to a real case as it may be the monitoring of ionospheric scintillation in Antarctica.
CITATION STYLE
Giordanengo, G., Pilosu, L., Mossucca, L., Renga, F., Ciccia, S., Terzo, O., … Hunstad, I. (2018). Energy efficient system for environment observation. In Advances in Intelligent Systems and Computing (Vol. 611, pp. 987–999). Springer Verlag. https://doi.org/10.1007/978-3-319-61566-0_93
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