Recent deployments of Smart Water Networks in urban environments are causing a paradigm shift towards sustainable water resources management. Nevertheless, there exists a substantial gap on respective solutions for industrial water treatment. In such deployments the wireless network backbone would have to overcome limiting factors that span across different layers of a protocol stack. Incorporating data analytics for capturing multi-dimensional correlations could be extremely beneficial to the design of reconfigurable network protocols for industrial Smart Water Networks. In this work, we exploit recent findings in the arena of network measurements and we propose a graph-based unsupervised feature selection approach for extracting the dominant network conditions that affect the performance of user-defined links. We employ a real- life industrial Smart Water Network deployed in a desalination plant to evaluate the efficacy of our approach. Finally, we provide useful insights on how different locations in a desalination plant affect the performance of the network backbone.
['Athanasia Panousopoulou', 'Panagiotis Tsakailides']
On graph-based feature selection for multi-hop performance characterization in industrial smart water networks