Spatio-temporal correlations for damages identification and localization in water pipeline systems based on WSNs

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.