Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
Daniele Strigaro,
Massimiliano Cannata,
Fabio Lepori,
Camilla Capelli,
Andrea Lami,
Dario Manca,
Silvio Seno
Affiliations
Daniele Strigaro
Department of Earth and Environmental Sciences (DSTA), University of Pavia, Via Ferrata 9, 27100 Pavia, Italy
Massimiliano Cannata
Institute of Earth Sciences, Department of Environment, Construction and Design, University of Applied Sciences of Southern Switzerland (SUPSI), Campus Mendrisio, Via Francesco Catenazzi 23, 6850 Mendrisio, Switzerland
Fabio Lepori
Institute of Earth Sciences, Department of Environment, Construction and Design, University of Applied Sciences of Southern Switzerland (SUPSI), Campus Mendrisio, Via Francesco Catenazzi 23, 6850 Mendrisio, Switzerland
Camilla Capelli
Institute of Earth Sciences, Department of Environment, Construction and Design, University of Applied Sciences of Southern Switzerland (SUPSI), Campus Mendrisio, Via Francesco Catenazzi 23, 6850 Mendrisio, Switzerland
Andrea Lami
National Research Council of Italy, Water Research Institute (CNR-IRSA), Largo Tonolli 50, 28922 Verbania, Italy
Dario Manca
National Research Council of Italy, Water Research Institute (CNR-IRSA), Largo Tonolli 50, 28922 Verbania, Italy
Silvio Seno
Department of Earth and Environmental Sciences (DSTA), University of Pavia, Via Ferrata 9, 27100 Pavia, Italy
In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.
