Hung Cao

The transportation phase in off-site construction subjects prefabricated modules to road-induced vibrations, shocks, and handling loads that can degrade structural integrity. Existing monitoring approaches often rely on local data loggers, which not only lack real-time visibility but also fail to link structural responses to their precise spatial and temporal context. To address this gap, this study proposes a GPS-integrated Internet of Things (IoT) framework for real-time monitoring of prefabricated modules during transit. The system comprises distributed inertial sensing nodes wirelessly connected to a central gateway, which aggregates and transmits synchronized sensor and GPS data to a cloud platform for analysis and visualization. Field validation demonstrated stable multi-node data acquisition with sufficient battery life to support extended monitoring under LTE connectivity. The framework supports dual-stream analytics: (i) time- and frequency-domain assessment of structural exposure using peak acceleration, RMS, and FFT metrics, and (ii) causal inference of road events (e.g., potholes, bumps, sharp turns). Vertical acceleration emerged as the most responsive diagnostic channel for capturing road-induced excitations, while gyroscope-derived motion profiles distinguish between driver maneuvers and road irregularities. Through seamless integration of structural and geospatial data in a scalable, low-cost system, this framework enables actionable insights for route planning, condition-based inspection, and improved logistics management in modular construction.

This paper presents our submission to the ACMMM25 - Grand Challenge on Multimedia Verification. We developed a multi-agent verification system that combines Multimodal Large Language Models (MLLMs) with specialized verification tools to detect multimedia misinformation. Our system operates through six stages: raw data processing, planning, information extraction, deep research, evidence collection, and report generation. The core Deep Researcher Agent employs four tools: reverse image search, metadata analysis, fact-checking databases, and verified news processing that extracts spatial, temporal, attribution, and motivational context. We demonstrate our approach on a challenge dataset sample involving complex multimedia content. Our system successfully verified content authenticity, extracted precise geolocation and timing information, and traced source attribution across multiple platforms, effectively addressing real-world multimedia verification scenarios.