Solar energy prediction and task scheduling for wireless sensor nodes based on long short term memory

Abstract

Traditional wireless sensor networks (WSNs) with limited energy capacity suffer from bounded lifetimes, which have become a major restriction on the development of WSNs. As a common renewable energy, solar energy instead of ordinary batteries can solve energy supply problem in wireless sensor nodes by transferring ambient energy to usable electrical power. In the process of solar energy harvesting, energy prediction is an essential precondition to ensure the reasonable task scheduling of wireless sensor nodes. As the uncertainty of solar energy, we present a long short term memory recurrent neural network (LSTM-RNN) solar energy prediction method, to predict solar energy in the next three days based on historical solar energy collection data and environmental data. Based on energy prediction results, the predictive task scheduling strategy is put forward to improve the performance of wireless sensor nodes. Experimental results show that the proposed LSTM-RNN solar energy prediction method has higher precision and better convergence than other conventional methods. The predictive strategy significantly increases the task completion rate of the wireless sensor node by using the prediction energy information while maintaining an approximate survival time.