Abstract
Environmental wireless sensor network (EWSN) nodes are ordinarily implemented as embedded devices which use low-power and low-cost microcontrollers whose duty cycle is controlled by a machine learning method. This article updates a previously designed Q-learning (QL) algorithm to reduce the number of failed operational duty cycles in solar-powered EWSN nodes. The innovation is based on modification of the QL learning process and was tested and verified on an EWSN node model using a 5-year dataset of solar irradiance values. By designing a new learning algorithm, the number of failures in operational cycles was significantly reduced, specifically by a factor of ten compared to the reference algorithm.
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