Secure Edge Computing Reference Architecture for Data-driven Structural Health Monitoring: Lessons Learned from Implementation and Benchmarking

2025-03-24

AI Summary

Structural health monitoring (SHM) faces challenges in edge-deploying machine learning (ML) models—including hardware heterogeneity, insufficient security and scalability, and vendor lock-in. Method: This paper proposes the first open-source, cross-platform edge computing reference architecture specifically designed for SHM, coupled with edgeOps, a novel resource-aware benchmarking framework enabling systematic performance evaluation and model selection optimization across heterogeneous edge devices. Evaluation leverages commercial data acquisition systems, commodity Linux/ARM hardware, open-source edge platforms, and cloud-based management, measuring CPU utilization, memory footprint, and inference latency. Contribution/Results: Validated on the real-world Living Bridge (New Hampshire Memorial Bridge), the architecture significantly improves deployment flexibility and inference reliability while reducing end-to-end latency. It establishes a reusable engineering paradigm and provides quantitative, evidence-based guidance for ML model and hardware selection in edge-enabled SHM systems.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Structural Health Monitoring (SHM) plays a crucial role in maintaining aging and critical infrastructure, supporting applications such as smart cities and digital twinning. These applications demand machine learning models capable of processing large volumes of real-time sensor data at the network edge. However, existing approaches often neglect the challenges of deploying machine learning models at the edge or are constrained by vendor-specific platforms. This paper introduces a scalable and secure edge-computing reference architecture tailored for data-driven SHM. We share practical insights from deploying this architecture at the Memorial Bridge in New Hampshire, US, referred to as the Living Bridge project. Our solution integrates a commercial data acquisition system with off-the-shelf hardware running an open-source edge-computing platform, remotely managed and scaled through cloud services. To support the development of data-driven SHM systems, we propose a resource consumption benchmarking framework called edgeOps to evaluate the performance of machine learning models on edge devices. We study this framework by collecting resource utilization data for machine learning models typically used in SHM applications on two different edge computing hardware platforms. edgeOps was specifically studied on off-the-shelf Linux and ARM-based edge devices. Our findings demonstrate the impact of platform and model selection on system performance, providing actionable guidance for edge-based SHM system design.

Problem

• Deploying scalable ML models for real-time SHM at edge
• Overcoming vendor lock-in with open-source edge platforms
• Benchmarking resource use of SHM models on edge devices

Innovation

• Secure edge-computing architecture for SHM
• Integration of commercial and open-source platforms
• Resource benchmarking framework for edge devices