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• W2608561329 abstract "The critical infrastructures such as power distribution networks (PDN), water networks, transportation and telecommunication networks that are settled within the area of a city produce a large amount of data from applications such as AMI, SCADA, Renewable Energy Management Systems, Asset Management Systems, and weather data. To convert these massive data into useful information, visualization is an effective solution. Visualizing this large amount of data in a holistic view of critical infrastructures mapping at a city level is a missing link. Visualization means here to convert the flow of continuous coming data into useful information. In this paper we propose a technique to visualize critical infrastructure data by using a system that consists of Geographic Information System (GIS) for buffer spatial analysis and Google Earth in sync with realistic planning and operation methodologies specific for each infrastructure modelled. The major goal of this work is to design, model and validate a benchmark system that is capable to visualize and map as well as to prepare the next inter-linking phase of modelling and analysis of interdependencies of several critical infrastructures. Furthermore, we aim to provide the grounds for a theoretical framework that can capture the interdependencies between critical infrastructures using techniques from graph theory, machine learning, econometric science and operation research. The proposed framework for modeling the interdependencies between several infrastructures within a city territory is based on hybrid system automata and it is among the first steps needed in developing fundamental mechanisms for resilient management of critical infrastructures and the safe operation of smart cities. An example on how this framework can be applied is also presented." @default.
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• W2608561329 date "2017-01-01" @default.
• W2608561329 modified "2023-09-24" @default.
• W2608561329 title "Test System for Mapping Interdependencies of Critical Infrastructures for Intelligent Management in Smart Cities" @default.
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• W2608561329 doi "https://doi.org/10.1007/978-3-319-54558-5_17" @default.
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