Structural Health Monitoring (SHM) is crucial for the safety and maintenance of various infrastructures. Due to the large amount of data generated by numerous sensors and the high real-time requirements of many applications, SHM poses significant challenges. Although the cloud-centric stream computing paradigm opens new opportunities for real-time data processing, it consumes too much network bandwidth. In this paper, we propose ECStream, an Edge Cloud collaborative fine-grained stream operator scheduling framework for SHM. We collectively consider atomic and composite operators together with their iterative computability to model and formalize the problem of minimizing bandwidth usage and end-to-end operator processing latency. Preliminary evaluation results show that ECStream can effectively balance bandwidth usage and end-to-end operator computation latency, reducing bandwidth usage by 73.01% and latency by 34.08% on average compared to the cloud-centric approach.

翻译：结构健康监测（SHM）对于各类基础设施的安全与维护至关重要。由于大量传感器生成的海量数据以及众多应用对高实时性的需求，SHM面临着重大挑战。尽管以云为中心的流计算范式为实时数据处理带来了新机遇，但其消耗过多的网络带宽。本文提出ECStream——一种面向SHM的边缘云协同细粒度流算符调度框架。我们综合考虑原子算符与复合算符及其迭代可计算性，对最小化带宽使用与端到端算符处理延迟问题进行建模与形式化。初步评估结果表明，ECStream能有效平衡带宽使用与端到端算符计算延迟，与云中心方法相比，平均减少带宽使用73.01%，降低延迟34.08%。