Edge Cloud Collaborative Stream Computing for Real-Time Structural Health Monitoring

TL;DR

This paper introduces ECStream, a collaborative edge-cloud framework for real-time structural health monitoring that significantly reduces bandwidth and latency compared to traditional cloud-centric methods.

Contribution

The paper presents a novel ECStream framework that optimizes operator scheduling for SHM, balancing bandwidth and latency through a formalized approach.

Findings

Reduces bandwidth usage by 73.01%

Decreases latency by 34.08%

Effective balance between bandwidth and latency achieved

Abstract

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.