IoT-Enabled Disaster Prevention: Leveraging Transfer Learning for Remote Monitoring

This research aims to develop an effective solution for disaster monitoring by utilizing transfer learning within a virtualized prototype. The main goal is to evaluate how transfer learning can enhance data management and analysis in remote and disaster-prone areas where data volume and transmission efficiency are crucial. The research focuses on two primary technical objectives: first, constructing a virtualized prototype to validate the proposed solution through simulation of disaster scenarios; and second, applying advanced artificial intelligence algorithms to process and interpret the collected data. The virtualized prototype will support empirical analysis of monitoring systems’ feasibility and efficiency in challenging environments. The specific objectives include: (i) analyzing transfer learning techniques for remote monitoring, (ii) creating an IoT architecture that replicates geographic and communication constraints, and (iii) deploying machine learning algorithms to effectively predict and manage disaster scenarios.