An Energy Harvesting Roadside Unit communication load prediction and energy scheduling based on graph convolutional neural networks for spatial‐temporal vehicle data

Abstract

The Energy Harvesting Roadside Unit (EH-RSU) with self-powered module will not only effectively reduce the communication load of regional Vehicular Ad Hoc Networks, but also enjoys a low deployment cost. Given the imbalance in communication demands invoked by transportation systems, the EH-RSU should allocate energy appropriately in accordance with its energy harvesting rate to ensure the communication safety of vehicles within its coverage. Firstly, we propose a novel attention-based spatial-temporal graph convolutional network (ASTGCN) to predict the communication load around the EH-RSU in the road network through the surrounding vehicle information. Secondly, we use the predicted communication load as part of the input parameters to neural network and leverage a double deep Q network to ameliorate the operating states switching strategy of EH-RSUs by reinforcement learning so that they achieve a more satisfying effective time with limited resources. Finally, we built a dataset by simulation to validate the effectiveness of our model. The results show that our prediction model has a better accuracy and the improved strategy has higher efficiency compared with other methods.