Advertisement

Algorithms and Tools for Intelligent Monitoring of Critical Infrastructure Systems

  • Cesare Alippi
  • Romolo Camplani
  • Antonio MarulloEmail author
  • Manuel Roveri
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 565)

Abstract

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.

Keywords

Environmental monitoring Critical infrastructure systems Sensor networks Energy harvesting Embedded systems Adaptive sensing 

Notes

Acknowledgment

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.

References

  1. 1.
    Farrar, C., Park, G., Allen, D.W., Todd, M.: Sensing network paradigms for structural health monitoring. J. Struct. Control Health Monit. 13(1), 210–225 (2006)CrossRefGoogle Scholar
  2. 2.
    Worden, K., Dulieu-Barton, J.M.: An overview of intelligent fault detection in systems and structures. Struct. Health Monit. 3(1), 85–98 (2004)CrossRefGoogle Scholar
  3. 3.
    Buttyan, L., Gessner, D., Hessler, A., Langendoerfer, P.: Application of wireless sensor networks in critical infrastructure protection: challenges and design options (security and privacy in emerging wireless networks). IEEE Wirel. Commun. 17(5), 44–49 (2010)CrossRefGoogle Scholar
  4. 4.
    Kostopoulos, D., Leventakis, G., Tsoulkas, V., Nikitakos, N.: An intelligent fault monitoring and risk management tool for complex critical infrastructures: the SERSCIS approach in air-traffic surface control. In: UKSim 14th International Conference on Computer Modelling and Simulation (UKSim), pp. 205–210, 2012Google Scholar
  5. 5.
    Caldeira, F., Schaberreiter, T., Monteiro, E., Aubert, J., Simoes, P., Khadraoui, D.: Trust based interdependency weighting for on-line risk monitoring in interdependent critical infrastructures. In: 6th International Conference on Risk and Security of Internet and Systems (CRiSIS), pp. 1–7, 2011Google Scholar
  6. 6.
    Schreiber, F., Camplani, R., Fortunato, M., Marelli, M., Rota, G.: Perla: a language and middleware architecture for data management and integration in pervasive information systems. IEEE Trans. Softw. Eng. 38(2), 478–496 (2012)CrossRefGoogle Scholar
  7. 7.
    Alippi, C., Camplani, R., Galperti, C., Marullo, A., Roveri, M.: An hybrid wireless-wired monitoring system for real-time rock collapse forecasting. In: IEEE 7th International Conference on Mobile Adhoc and Sensor Systems (MASS), pp. 224–231, 2010Google Scholar
  8. 8.
    Alippi, C., Galperti, C.: An adaptive system for optimal solar energy harvesting in wireless sensor network nodes. IEEE Trans. Circuits Syst. I Regul. Pap. 55(6), 1742–1750 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Commun. ACM 21(7), 558–565 (1978)CrossRefzbMATHGoogle Scholar
  10. 10.
    Alippi, C., Camplani, R., Roveri, M., Viscardi, G.: NetBrick: a high-performance, low-power hardware platform for wireless and hybrid sensor network. In: The 9th IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS 2012), 2012Google Scholar
  11. 11.
    Elahi, A., Gschwender, A.: ZigBee wireless sensor and control network. Prentice Hall, Upper Saddle River (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Cesare Alippi
    • 1
  • Romolo Camplani
    • 1
  • Antonio Marullo
    • 2
    Email author
  • Manuel Roveri
    • 1
  1. 1.Politecnico Di MilanoMilanItaly
  2. 2.Altran ItaliaMilanItaly

Personalised recommendations