Water Quality Prediction on a Sigfox-compliant IoT Device: The Road Ahead of WaterS

Abstract

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 enddevices, 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.