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PH.D. STUDIES

IOT-SHM

Research Overview

RESEARCH TOPICS -- PUSHING STRUCTURAL HEALTH MONITORING TO THE EDGE DEVICES

My Ph.D. research topic is Distributed Edge Intelligence Framework for IoT-based Structural Health Monitoring, with a focus on developing a distributed edge-intelligence enabling framework for resource-constrained, low-cost edge devices, to promote a paradigm shift from cloud to edge and from knowledge-driven to AI-driven approaches, and to apply it to typical IoT-based structural health monitoring applications.

KEY TASKS

  • IoT System Development: Edge Device (MCU Level, Low-cost & Resource-constrained) and Cloud Platform Development
  • Distributed Edge Intelligence Enabling Framework Development: TinySHM (Vector & Matrix Operations / Digital Signal Processing / Machine Learning & Artificial Intelligence Libraries)
  • Structural Health Monitoring Applications: Measurement / System Identification / Damage Detection / Damage Localization / Damage Evaluation

REVIEW-GA

  • Review Paper - Ubiquitous Computing and Intelligence in IoT-based Structural Health Monitoring (From Edge to Cloud)

    Cui, S., Fu, Y.*, Fu, H., & Shen, W. (2026). Edge-to-Cloud Computing and Intelligence for IoT-based Structural Health Monitoring: A Comprehensive Review. Advanced Engineering Informatics.

    DOI

I IoT-based SHM System Development

1.1 Edge Device

Edge Device Development

To achieve edge intelligence computing, we have developed two types of MCU nodes, based on STM32 and ESP32. These nodes have high-performance edge computing capabilities and can be used in IoT, smart home, smart city, and other application scenarios. The current development focus is on ESP32.

Sensor Extension Board

AIoTNode-Extension

Main Control Board Base

AIoTNode-Base

1.2 Cloud Platform

II Distributed Edge Intelligence Framework for Resource-Constrained Devices

III Emerging ML/AI Powered Typical Structural Health Monitoring Applications

3.1 Single-Node Independent Applications

- Smart Adaptive Trigger Sensing Powered by Edge Intelligence and Digital Twin for Energy-Efficient Wireless Structural Health Monitoring

🏷️ SHM Category: Measurement / System Identification / Damage Detection / Damage Localization / Damage Evaluation

  • Trigger Sensing
  • Closed-Loop Feedback Control
  • Edge Intelligence
  • Bayesian Optimization

  • Conference Paper - 13th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-13)

    Cui, S., Yu, X., & Fu, Y.* (2025). Smart adaptive triggering strategy for edge intelligence enabled energy-efficient sensing. In Proceedings of the 13th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-13), pp. 609–616. Graz, Austria: Verlag der TU Graz. (🏆 Best Conference Paper Award 1st/202)

    DOI

  • Journal Paper - Mechanical System and Signal Processing

    Cui, S., Fu, Y.*, Fu, H., Yu, X., & Shen, W. (2025). Smart Adaptive Trigger Sensing Powered by Edge Intelligence and Digital Twin for Energy-Efficient Wireless Structural Health Monitoring. Mechanical Systems and Signal Processing, Volume 241, 2025, 113537.

    DOI

  • Singapore Patent - 10202502426R

    Adaptive Triggering Mechanism for Time-Series Data Sensing on Edge Devices, Singapore provisional patent application number 10202502426R, 2025

3.2 Multi-Node Collaborative Applications

- Adaptive edge intelligence for rapid structural condition assessment using a wireless smart sensor network

🏷️ SHM Category: Measurement / System Identification /Damage Detection /Damage Localization /Damage Evaluation

  • Data-driven Anomaly Detection
  • Gaussian Process Regression (GPR)
  • Stochastic Process Control (SPC)

PAPER1

  • Journal Paper - Engineering Structures

    Cui, S., Hoang, T., Mechitov, K., Fu, Y.*, & Spencer Jr, B. F. (2025). Adaptive edge intelligence for rapid structural condition assessment using a wireless smart sensor network. Engineering Structures, 326, 119520.

    Portal