Testing and Proof of Concept for Automated Leak Detection Using Wireless Sensors: A Pilot Study for Johannesburg Water Company The main objective of automated leak detection is the deployment of wireless sensor network (WSN) system for the continuous monitoring of the urban water supply networks in pursuit to early detection of leaks in order to achieve reduction of real losses within water distribution networks. This research work provides insight on the processes and activities in the run up to the pilot test conducted at Midrand: Johannesburg (Country View) including the findings made on the status of the pipe network. The project is initiated by University of Johannesburg (UJ) and Johannesburg Water (JW) in collaboration with Process Energy u0026 Environmental Technology Station (PEETS) and SMEu0027s as part of digitilising water demand management in pursuit to reduction of high levels of NonRevenue Water. The performance of this innovative automated leak detection monitoring system has been tested and evaluated through a pilot deployment of wireless sensors in a controlled environment under real condition in the City of Johannesburg, Midrand Region. This paper is a continuation of the work done on integration of Small and Medium Enterprises for Industry 4.0 in the South African Water Services Sector specifically looking at business innovative water demand management technologies and entrepreneurship.

Spatiotemporal correlations for damages identification and localization in water pipeline systems based on WSNs Abstract   Pipeline leakages may conduct large excessive costs for reparation, infrastructure damage combined with environmental pollution. Consequently, the security and maintenance of the pipeline infrastructure are one of the major preoccupations for searchers. The most suitable are these solutions based on wireless sensors networks. However, WSNs are susceptible to noise effect, material defaults and malicious attacks from intruders. Therefore, it is essential to identify potential events like a leak, as well as erroneous and malicious attacks as defected sensors, occurred on the network. For that propose, we have presented a distributed one class classification technique for outliers detection based on WSNs. In addition, we have searched to improve our classifier by using a centered ellipsoidal technique to classify data. Then, we have demonstrated the importance of our improved technique comparing to other classifiers. Likewise, we have investigated the notion of the relationship existing between closed neighboring nodes and the existing correlation between historical observations to identify damages sources. These improvements led to increase the detection accuracy level and decrease the false alarm percentage by respecting WSNs constraints such as energy consumption.