This report delivers a practical manual to support operators with the management of sensors networks in existing water infrastructures. It includes (1) the presentation and assessment of a new easy-to-use sensor for faecal bacteria measurements, (2) methodologies for the validation of online sensors and analysers and (3) best practices for installation, operation, and maintenance. In DWC, raw data collected from on-line sensors and lab analyses are integrated and analysed to gather conclusive information and early warning to support decisions to deliver safe water reuse and inform about bathing water quality. Three relevant case studies, namely Paris, Berlin and Milan, were investigated in this research. In the case studies of Paris and Berlin, sensors were installed to monitor microbiological contamination in bathing water sites. In the case study of Milan, a real-time sensor network was designed to promote safe water reuse reducing the risk of microbial contamination of soils and crops during irrigation, while assuring compliance of wastewater quality with reuse standard limits. The technical characteristics of all the installed on-line sensors are reported in section 1, including the innovative ALERT devices manufactured by FLUIDION, which allow the on-line measurements of faecal bacteria indicators. The section also describes in detail measurement characteristics, i.e., static and dynamic characteristics of instrumentation, operational modes, initial measurement accuracy and standards. The use of real-time data to support health protection and risk management requires primary their validation, in terms of reliability, in order to integrate the standard lab measures with a continuous monitoring system, for control optimization and risk minimization. To date, one of the main lacks on risk management approach is the absence of common procedure on how to treat non-standardized data, such as real-time online data. To answer this question, this report intends to provide practical information about validation, operation and maintenance of on-line sensors for the three representative case studies. Particularly, this report includes Return of experience on installation, troubleshooting and maintenance (section 2). Data analysis and assessment of the bias, precision and accuracy of the online sensors (section 3). The conclusions are reported in section 4.