We propose a novel energy-aware approach to detect a leak and estimate its size and location in a noisy water pipeline using leastsquares\nand various pressuremeasurements in the pipeline network.Thenovelty in ourwork hinges on the fusion of the duty-cycling\n(DC) and data-driven (DD) strategies, both well-known techniques for energy reduction in a wireless sensor network (WSN). To\nmaximize the information gain and minimize the energy consumed by the WSN, we first study the effects of (a) various levels of\nsensor measurement uncertainty and (b) the use of the smallest possible number of pressure sensors on the overall accuracy of\nour approach. Using the DD strategy only, a noisy environment, and a small number of sensors, the performance of our scheme\nshows that, for small leak sizes, the estimation error in both leak location and size becomes unacceptably high. Next, using as few\nsensors as possible for an acceptable accuracy, we fused the DD strategy with the DC one to minimize the sensing, processing,\nand communication energies. The fusion approach yielded a better performance with significant energy saving, even in noisy\nenvironments. EPANET was used to model the pipeline network and leak and MATLAB to implement, analyze, and evaluate our\nfusion approach.
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