Augmented Information Discovery using NFC Technology within a Platform for Disaster Monitoring

Catastrophic and accidental natural events (e.g., earthquakes, sudden floods, fires, etc.) and deficiencies in appropriate management activities require proper plans for disaster management and real-time definition of safe paths and escape routes. Emergencies can affect the structural health status of structures and infrastructures and, consequently, the safety in highly populated areas (e.g., urban contexts, occasional assembly points for crowds, theme parks, etc.) is compromised. Consequently, the use of a decision platform for the management of structures and infrastructures, which is able to gather real-time data from sensors and provide alerts and augmented information about safe paths, paves the way to the adoption of a proactive form of risk management. In this work, objectives were confined into the validation of a Near-Field Communication (NFC) system. Data coming from user smartphones using the NFC technology and sensor data deriving from the decision platform were used as input in a Machine Learning (ML) algorithm. Results show that the ML algorithm mentioned above is able to refine the safe path recognition strategy defined from the platform. In addition, the bidirectional and touch less NFC technology allows the delivery of alerts and disaster plan dissemination, also in the case of connectivity shutdown.