Two-Stage Energy Prediction With Prior Estimation and Dynamic Adaptation for Symbiotic IOV

In the framework of 6G-driven Symbiotic Internet of Things (Symbiotic IoT), vehicular edge computing systems enhance the inference efficiency of large-scale AI models through collaboration between road side units (RSUs) and in-vehicle terminals. As a core component of symbiotic systems, RSUs face intermittent challenges posed by renewable energy supply when hosting offloaded vehicular deep learning tasks. Existing energy consumption prediction methods rely heavily on massive sampled data and lack a priori evaluation, leading to significant difficulties and risks in acquiring large-scale measured data: when the remaining power of RSUs is insufficient to sustain the sampling period, the prediction process is forced to interrupt, wasting both the operational energy already consumed and the additional scheduling computation energy. This article proposes a two-stage energy consumption prediction method for symbiotic Internet of Vehicles (IoV) edge nodes, addressing the above challenges through a collaborative mechanism of “a priori rough estimation—dynamic precise prediction.” In the first stage, a nonlinear regression model is established based on limited measured data, combined with analysis of model architecture and hardware instruction-level energy consumption characteristics, to enable a priori energy estimation without actual model execution. This supports lightweight decision-making for RSUs during task allocation, reducing computational overhead compared to traditional sampling methods. In the second stage, real-time energy consumption monitoring is used to dynamically update prediction curves, achieving prediction calibration within extremely short operation cycles to balance prediction accuracy with the energy constraints of edge nodes.Experimental results in typical scenarios of IOV applications demonstrate that the proposed a priori evaluation method achieves an average error of 10% under different data volumes and batch sizes, while the real-time prediction method yields short-term and long-term prediction errors below 3% and 6%, respectively—outperforming existing methods in accuracy. This approach effectively assists RSUs in energy management, reducing energy waste caused by the absence of a priori evaluation and prediction errors, and contributes to the efficient utilization of renewable energy in low-latency scenarios, such as connected vehicles.