Algorithms and Tools for Intelligent Monitoring of Critical Infrastructure Systems
Critical Infrastructure Systems (CIS) are essential services to sustain both society and economy. In fact, CIS can be considered as vital systems for a geographic area or a country. Such valuable assets have to be carefully monitored because their partial or complete failure (caused by natural hazards or criminal acts) could produce severe costs in terms of environment, economy and, in the worst scenario, human lives. The need to protect and maintain CIS and the surrounding environment is pushing the research for the development of intelligent monitoring systems, able to detect anomalies and events and to adapt autonomously to the changes in the system under investigation. In this chapter, we describe an intelligent hardware-software architecture for CIS monitoring, specifically designed for asynchronous events detection, remote configurability and diagnosis. In particular, this monitoring system is based on a novel hybrid architecture, in which different sensors, architectures and physical phenomena under monitoring coexist and cooperate to provide different views of the same physical phenomenon. In fact, the proposed monitoring system is able to gather both high frequency signals (microscopic level), such as accelerometer signals, and low-dynamic signals (macroscopic level), such as temperature and inclination. The monitoring system is connected to a remote data center, which collects, interprets and forwards them to the stakeholders in the desired format. The design principles driving the monitoring system are introduced. As a practical application will be shown a CIS monitoring system employed to monitor the Rialba’s tower, a rock tower-like limestone complex overlooking an area of strategic importance connecting the Lecco and Como provinces in north Italy. The rock tower is indeed exposed to a rock toppling risk, thus menacing an area characterized by the presence of a freeway, a railway line and gas and power distribution pipelines.
KeywordsEnvironmental monitoring Critical infrastructure systems Sensor networks Energy harvesting Embedded systems Adaptive sensing
This work has been partially supported by the EU INTERREG project Italy-Switzerland action 2007–2013 MIARIA (Project Id 7629775) and the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation, KIOS project.
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