  Water pollution is a critical issue that can affects humans' health and the
entire ecosystem thus inducing economical and social concerns. In this paper,
we focus on an Internet of Things water quality prediction system, namely
WaterS, that can remotely communicate the gathered measurements leveraging
Low-Power Wide Area Network technologies. The solution addresses the water
pollution problem while taking into account the peculiar Internet of Things
constraints such as energy efficiency and autonomy as the platform is equipped
with a photovoltaic cell. At the base of our solution, there is a Long
Short-Term Memory recurrent neural network used for time series prediction. It
results as an efficient solution to predict water quality parameters such as
pH, conductivity, oxygen, and temperature. The water quality parameters
measurements involved in this work are referred to the Tiziano Project dataset
in a reference time period spanning from 2007 to 2012. The LSTM applied to
predict the water quality parameters achieves high accuracy and a low Mean
Absolute Error of 0.20, a Mean Square Error of 0.092, and finally a Cosine
Proximity of 0.94. The obtained results were widely analyzed in terms of
protocol suitability and network scalability of the current architecture
towards large-scale deployments. From a networking perspective, with an
increasing number of Sigfox-enabling end-devices, the Packet Error Rate
increases as well up to 4% with the largest envisioned deployment. Finally, the
source code of WaterS ecosystem has been released as open-source, to encourage
and promote research activities from both Industry and Academia.
