## 5 Conclusions  
This paper presented an IoT-enabled platform, designed based on the fog computing paradigm, for monitoring and controlling water distribution grids. The key element of the design approach was the introduction of an intermediate layer, between remote cloud data centres and the smart metering devices, capable of executing computational tasks. This intermediate layer was connected to the network backbone based on long-range low-power networking technologies. The platform implemented a generic execution environment that was flexible enough to accommodate a broad range of data analytic tasks that could be executed on this intermediate layer. These tasks were deployed at the remote cloud data centres and were delivered to the intermediate layer of execution. The platform was deployed in a real-world environment comprised of several smart metering devices and smart valves. The performance of the deployment was evaluated both in terms of network connectivity and computational performance. The experimental evaluation indicated that with proper fine-tuning of the various parameters, the intermediate layer could operate over narrow-band connectivity, enhance the reliability of the deployment and reinforce the platform security. In particular, the results indicated that stable connectivity was maintained for the majority of the period. At the same time, the intermediate layer was used to analyse the data collected from the smart metering devices, thus significantly reducing the amount of data that needed to be delivered to the cloud data centres. As future work, we would like to extend the deployment by introducing additional devices comprised of heterogeneous hardware capabilities, possibly combining also a GPU. In such a heterogeneous intermediate layer, it is important that the execution environment dynamically identifies the most suitable location where the computational tasks can be executed given the network and computational load of the platform. Given such an intermediate processing layer, it is important to implement different data analysis tools to enrich water consumption information and assist water utilities and their clients in obtaining benefits from data sampling and the cost of high-resolution metering and real-time data-model coupling. The availability of such data analysis tools at the edges of the network will also enable the development of richer, direct feedback mechanisms to help reduce water usage and thus benefit the customers; billing and costs. \begin{table}
\begin{tabular}{l c} \hline \hline
**Device** & **Processing Rate** \\ \hline Tergo Raspberry & 15.36 \\ Tergo Zotac & 2692.31 \\ Tethys & 5833.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Processing rate in messages per second. The work presented in this paper focused on the benefits of the fog computing paradigm in terms of processing the data collected from the smart water metering devices. Important future work is to examine the levels of cyber-security and cyber-protection achieved. As a system composed of different smart devices, with data processing carried out at remotely located micro-data centres, guaranteeing the overall security of the system is a challenge [62; 103; 104]. Towards this direction, we need to develop efficient methods and technologies to benchmark the smart water micro-components' protection efficiency. Finally, an important future research direction is to continue the operation of the pilot deployment in order to collect long-term data related to the actual data usage and the network performance. Smart water grids have an increased complexity compared to traditional water infrastructures. Therefore, more real-world tests will help better realize the resilience of the smart water system related to automatic and online operations. Conceptualization and methodology, D.A., I.C., and S.P. ; software, D.A., N.T., and N.N. ; writing, original draft preparation, D.A., I.C., and C.T. ; writing, reviewing and editing, I.C. and C.T. ; data curation and visualization, D.A., N.T., and N.N.