Optimizing of Q-learning day/night energy strategy for solar harvesting environmental wireless sensor networks nodes

Abstract

This research article presents the application of the Q-learning algorithm in the operational duty cycle control of solar-powered environmental wireless sensor network (EWSN) nodes. Those nodes are commonly implemented as embedded devices using low-power and low-cost microcontrollers. Therefore, there is a significant need for an effective and easy way to implement a machine learning (ML) algorithm in terms of computer performance. This approach uses a Q-learning-based policy implementing a sleep/run switching algorithm driven by the state of charge. The presented algorithm is based on two modes: daylight and nighttime, which is a suitable solution for solar-powered systems. The study includes the complete process of design EWSN node strategy with an optimal reward policy. The presented algorithm was tested and verified on an EWSN node model and a 5-year data set of solar irradiance values was used for the learning process and its validation. As part of the study, we are also presenting the validation in terms of Q-learning parameters, which include the learning rate and discount factor. The result section shows that the overall performance of the presented solution is more suitable for solar-powered EWSN then state-of-the-art studies. Both day/night experiments reached 828 203 measurement/transmission cycles, which is 12.7 % more than in the previous studies using the strategy defined by the state of energy storage.