Spatio-temporal data prediction is a fundamental task in urban computing, benefiting a variety of real-life applications such as traffic forecasting and environmental monitoring. Due to the dynamic and time-involving nature of spatio-temporal data, researchers have increasingly emphasized online prediction. However, existing approaches (e.g., URCL) typically rely on data-replay strategies, which require storing large volumes of historical data to frequently update their models with new inputs. These methods impose substantial costs, including frequent buffer construction, high storage requirements, and increased training complexity. Furthermore, the single-pass nature of online data, combined with the constrained resources of online environments, highlights the urgent need for more efficient and lightweight solutions for online spatio-temporal prediction. To address these challenges, we propose Storm, a knowledge distillation driven continual learning framework. Storm introduces Dynamic Knowledge Distillation (DKD), leveraging an ever-evolving teacher model to train an effective student model. To optimize efficiency, Storm employs a Mixture-of-Experts (MoE) mechanism, which dynamically switches between the original training mode and the DKD mode. This hybrid design enables low-cost online learning while addressing the stabilityplasticity dilemma. To fully leverage single-pass online data, Storm integrates effective data augmentation methods tailored to the dynamic nature of spatio-temporal data. Moreover, Storm incorporates a Gradual Parameter Freezing (GPF) module to progressively reduce computational costs during online training. Extensive experiments conducted on four real-world datasets, evaluated across short-term, medium-term, and long-term prediction horizons, demonstrate the superiority of Storm. Specifically, Storm: (i) provides a general online training extension for various offline spatio-temporal models, and (ii) achieves remarkable improvements, e.g., up to 14.24% accuracy gains while requiring only 0.3% of the training and inference time compared to the state-of-the-art URCL framework. The source code is publicly available at https://github.com/ZJU-DAILY/Storm.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.