The processing of data and signals provided by sensors aims at extracting rnrelevant features which can be used to assess and diagnose the health state rnof the monitored targets. Nevertheless, Wireless Sensor Networks (WSNs) present rna number of shortcomings that have an impact on the quality of the gathered rndata at the sink level, leading to imprecise diagnostics rnof the observed targets. To improve data accuracy, two main critical and related issues, namely the energy consumption and coverage quality, need to be considered. The goal is to maximize the network lifetime while guaranteeing the complete coverage of all the targets. Unfortunately, these performance objectives are difficult in their own and solving them together makes the problem even harder. For instance, the energy consumptionrnwill increase if we want to enhance the coverage quality, even if no actual failure happens duringrnthe monitoring activity of the deployed network. To tackle this problem, many algorithms have been proposed in the literature but none of them has studied the impact of energy saving and target coverage on the quality of the data provided by a WSN. In this paper, we present a distributed algorithm based on a theory of domination in graphs, and we study its impact on diagnostics by using six machine learning algorithms. First, we give the correctness proofs and next we assess its behavior through simulations. Obtained results show that the proposed algorithm, despite its lower complexity, exhibits better performances than its direct competitor, the Probabilistic Coverage Protocol PCP, and the optimistic solution (which is called BaseLine).