  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.
