PROMETEO: Infrastructure Remote-Control and Geophysical Monitoring System of the INGV Osservatorio Vesuviano

The continuous operation of the geophysical monitoring networks managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV)–Osservatorio Vesuviano relies on the reliability of a highly distributed infrastructure deployed in active volcanic areas. In these contexts, failures affecting power supply, environmental control, or communication systems may lead to interruptions in data transmission and consequent loss of scientific observations. This work presents PROMETEO, an integrated remote-control and infrastructure monitoring system designed to supervise heterogeneous monitoring stations through a multiparametric sensing approach. The system combines distributed sensors and intelligent edge devices for the acquisition of electrical, environmental, and connectivity-related parameters, including battery voltage, load current, cabinet temperature, signal quality, and network reachability. Data are collected and integrated in real time through standard Internet of Things (IoT) and industrial communication protocols, namely Message Queuing Telemetry Transport (MQTT), Simple Network Management Protocol (SNMP), and MODBUS, and centralized within the open-source Home Assistant platform. This architecture enables the fusion of heterogeneous sensor measurements into a unified supervisory framework for real-time visualization, alarm generation, historical storage, and trend analysis. The results show that the multiparametric correlation of sensor data significantly improves diagnostic capability, allowing rapid discrimination between power-related anomalies and communication failures, particularly in remote mobile stations. By reducing diagnostic uncertainty and limiting unnecessary field interventions, PROMETEO enhances the operational resilience of geophysical monitoring infrastructures and supports preventive and predictive maintenance strategies. The proposed system demonstrates how a scalable multiparametric sensing architecture can strengthen the reliability and continuity of monitoring networks operating in complex environmental conditions.