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The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and …