Optimizing Structural Health Monitoring Systems Through Integrated Fog and Cloud Computing Within IoT Framework

Abstract

In this paper, we propose the design, operation, and implementation of an Internet of Things-based hybrid structural health monitoring. This innovative system leverages the capabilities of both fog and cloud layers in computing and monitoring. The system architecture consists of leaf nodes deployed on a target structure. These nodes, synchronously, collect acceleration signals from accelerometers attached directly to the structure and transmit the data to an on-site central node via a short-range communication protocol. At the fog layer, the central node, applies damage detection algorithms on the collected data. If a damage is detected, it forwards the acceleration signals to a cloud-based monitoring server using cellular internet connectivity, where more complex algorithms are used to identify and locate the damage. We provide detailed information about the design of the different system nodes, the implementation of damage detection algorithms, and the architecture of the monitoring server. To evaluate the effectiveness of the proposed system, several practical experiments were conducted. The results demonstrate that the hybrid system presented in this paper provides an efficient, reliable and cost-effective approach to damage detection and identification in civil infrastructures.