Sensor Systems and Software. 7th International Conference, S-Cube 2016, Sophia Antipolis, Nice, France, December 1-2, 2016, Revised Selected Papers

Research Article

Monitoring Approach of Cyber-Physical Systems by Quality Measures

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  • @INPROCEEDINGS{10.1007/978-3-319-61563-9_9,
        author={Pedro Merino Laso and David Brosset and John Puentes},
        title={Monitoring Approach of Cyber-Physical Systems by Quality Measures},
        proceedings={Sensor Systems and Software. 7th International Conference, S-Cube 2016, Sophia Antipolis, Nice, France, December 1-2, 2016, Revised Selected Papers},
        proceedings_a={S-CUBE},
        year={2017},
        month={7},
        keywords={Monitoring Sensor data processing Multi-source sensor network Cyber-physical system Data quality Information quality},
        doi={10.1007/978-3-319-61563-9_9}
    }
    
  • Pedro Merino Laso
    David Brosset
    John Puentes
    Year: 2017
    Monitoring Approach of Cyber-Physical Systems by Quality Measures
    S-CUBE
    Springer
    DOI: 10.1007/978-3-319-61563-9_9
Pedro Merino Laso1,*, David Brosset,*, John Puentes,*
  • 1: École navale - CC 600
*Contact email: pedro.merino@ecole-navale.fr, david.brosset@ecole-navale.fr, john.puentes@telecom-bretagne.eu

Abstract

Modern cities, industrial plants, cars, trucks, and vessels, among others, make extensive use of cyber-physical systems and sensors. These systems are very critical and contribute to assist decision making. Large data streams are thus produced and analyzed to extract information that allows building knowledge through a set of principles called wisdom. However, because of multiple imperfections, as well as intrinsic, contextual, and extrinsic conditions that alter data, the quality of the generated streams must be evaluated, to determine how relevant they are for decision support. This paper presents a methodology to monitor cyber-physical systems by quality estimation, which defines suitable evaluation characteristics for pertinent analysis. Quality assessment is defined for data imperfections, information dimensions, knowledge factors, and wisdom aspects. The case study of a cyber-physical network of a liquid container training platform is presented in detail, to show how the approach can be applied. Obtained measures are multidimensional, heterogeneous, and variable.