Alberto Tofani

The DIESIS Approach to Semantically Interoperable Federated Critical Infrastructure Simulation

Critical infrastructures (CI) such as telecommunication or the power grids and their dependencies are getting increasingly complex. Understanding these – often indirect –dependencies is a vital precondition for the prevention of cross sector cascading failures of CI. Simulation is an important tool for CI dependency analysis, the test of methods for risk reduction, and as well for the evaluation of past failures. Moreover, interaction of such simulations with external threat models, e.g., a river flood model and economic models, may assist in what-if decision-making processes. The simulation of complex scenarios involving several different CI sectors requires the usage of heterogeneous federated simulations of CI. However, common standards for modelling and interoperability of such federated CI simulations are missing. In this paper, we present a novel approach for coupling CI simulations, developed and realised in the EU project DIESIS. The DIESIS core technologies for coupling CI simulations include a middleware that enables semantic interoperability of the federate simulators, a systematic, service-oriented approach to set up and run such federations, and, most importantly, a scenario-based architecture concept for modelling and federated simulation of CI. The architecture foresees a flexible pair-wise (lateral) coupling of simulators. DIESIS has implemented a demonstrator as a proof of concept for its approach and technologies, by coupling four different simulation systems (three interacting CIs and an external, common threat). In this paper, we focus on the architectural concept and the interoperability middleware that realises this concept and allows the coupling of heterogeneous simulation systems using various time and data models. We show how the ontology-based Knowledge Based System (KBS) is integrated and used in the overall system. Then, we discuss the basic technical concepts as well as the results obtained with the demonstrator. The proposed architecture is open for further extensions. Ultimately, the proposed approach shall form the basis of a future standard coupling middleware for federated CI simulations.

An ontological approach to simulate critical infrastructures

Abstract This paper presents a Knowledge Base System (KBS) as a key component of a federated simulation framework aimed at investigating the dependencies among Critical Infrastructures (CIs). The KBS, based on the ontological formalism, represents the properties and the relations of each simulation domain and the dependency relations among different domains. Some auxiliary data structures, necessary to model the interaction among the simulators of different CIs, have been defined and have been populated through suitable queries to the KBS. The adoption of the ontological formalism allowed the definition of a common formalism to deal with the heterogeneity arising from the presence of different domains.

ONTOLOGY-BASED CRITICAL INFRASTRUCTURE MODELING AND SIMULATION

This paper describes a knowledge-based system (KBS) designed to support a federated environment for simulating critical infrastructure models. A federation of simulators is essentially a “system of systems,” where each simulator represents an entity that operates independently with its own behavior and purpose. The interactions among the components of the federated system of systems exhibit critical infrastructure vulnerabilities as emergent behavior; these vulnerabilities cannot be analyzed and simulated by considering the behavior of each system component individually. The KBS, which is based on ontologies and rules, provides a semantic foundation for the federated simulation environment and enables the dynamic binding of different critical infrastructure models. The KBS-based simulation environment can be used to identify latent critical infrastructure interdependencies and to test assumptions about interdependencies.

A Platform for Disaster Response Planning with Interdependency Simulation Functionality

Catastrophic events can result in great loss of lives and property. Planning an effective disaster response to minimize associated losses is a fundamental challenge for decision makers. The planning process can be improved by simulating interdependent critical infrastructures and evaluating system behavior during disaster scenarios. This paper describes a disaster response planning simulation platform that supports decision making based on the interdependencies existing between a power grid and a supervisory control and data acquisition (SCADA) system. By considering the physical constraints on the power grid and SCADA network, a set of feasible configurations is presented to disaster responders. The utility of the platform is demonstrated using an example scenario involving power distribution to a hospital during a disaster event.

The MIMESIS project: A decision support system for risk analysis and the impact evaluation of crisis scenarios of Critical Infrastructures deriving from extreme natural events

Extreme natural hazards are regarded as major threats for Critical Infrastructures (CI). A fault of single elements of a CI, due to CI interdependency, can produce hardly predictable cascading effects. Coupling meteorological-climatic and geophysical predictions with the knowledge of all CIs in a given region could be used to design a new class of Decision Support Systems (DSS) able to provide a dynamic risk assessment of the elements of all CIs and to estimate the impact that a specific crisis scenario could produce. We describe the MIMESIS tool, the prototype of such a new class of DSS, which includes tools for the prediction of crisis scenarios and for the evaluation of their impacts on a set of interdependent infrastructures, in terms of reduction of the delivered services and the impact that services unavailability might have on population. MIMESIS is currently under development and will constitute the kernel of a Center of Regional Services to deliver alert and support for crises prediction, their management, and the design of new strategies to mitigate their effects.

Using ontologies for the federated simulation of critical infrastructures

Abstract This paper presents a Knowledge Base System (KBS) as a key component of a federated simulation framework aimed at investigating the dependencies among Critical Infrastructures (CIs). The KBS, based on the ontological formalism, represents the properties and the relations of each simulation domain and the dependency relations among different domains. Some auxiliary data structures, necessary to model the interaction among the simulators of different CIs, have been defined and have been populated through suitable queries to the KBS. The adoption of the ontological formalism allowed the definition of a common formalism to deal with the heterogeneity arising from the presence of different domains.

Ontological Framework to Model Critical Infrastructures and their Interdependencies

This paper presents a Knowledge Base System (KBS) as a key component of a federated simulation framework which allows the investigation of (inter)dependencies among Critical Infrastructures (CIs). The KBS supports the federated simulation framework by using the ontological formalism to represent specific CI domains and their dependencies. The main advantage of the proposed ontological formalism consists on the abstraction of the description from the technological (i.e. simulation) level: the scenario can be described with a formalism used by the experts of the various CI domains.The proposed approach has been validated on a realistic scenario with actual data derived from the Rome city area. This scenario has been then used as testbed for a federated distributed simulation.

Distributed Localization Strategies for Sensor Networks

We consider the problem of localizing random sensor networks by means of time of arrival capabilities. Interactions among sensors are modeled by a mass-spring system. Masses (sensors) initially have a random estimation of their position. The knowledge of their distance from other masses is translated into connect masses with springs whose length should reach the estimated distances. This determines a set of forces to which masses are subject to. Starting from this configuration we propose several strategies reducing the overall time needed to reach the desired level of equilibrium.

Design of DSS for Supporting Preparedness to and Management of Anomalous Situations in Complex Scenarios

Decision Support Systems (DSS) are complex technological tools, which enable an accurate and complete scenario awareness, by integrating data from both “external” (physical) situation and current behaviour and state of functioning of the technological systems. The aim is to produce a scenario analysis and to guess identify educated the most efficient strategies to cope with possible crises. In the domain of Critical Infrastructures (CI) Protection, DSS can be used to support strategy elaboration from CI operators, to improve emergency managers capabilities, to improve quality and efficiency of preparedness actions. For these reasons, the EU project CIPRNet, among others, has realised a new DSS designed to help operators to deal with the complex task of managing multi-sectorial CI crises, due to natural events, where many different CI might be involved, either directly or via cascading effects produced by (inter-)dependency mechanisms. This DSS, called CIPCast, is able to produce a real-time operational risk forecast of CI in a given area; other than usable in a real-time mode, CIPCast could also be used as scenario builder, by using event simulators enabling the simulation of synthetic events whose impacts on CI could be emulated. A major improvement of CIPCast is its capability of measuring societal consequences related to the unavailability of primary services such as those delivered by CI.

Resources allocation in disaster response using Ordinal Optimization based approach

Recent events, such as Hurricane Katrina, have revealed the need for coordinated and effective disaster responses. An optimal distribution of available resources is essential for disaster response effectiveness. Emergency responders are faced with the challenges of increased size and complexity of critical infrastructures that provide vital resources for disaster response operations. In this paper, we propose a simulation-based tool to assist emergency responders in finding the optimal distribution of available resources during a disaster event. The proposed tool utilizes the Disaster Response Network Enabled Platform (DR-NEP) which is an infrastructure interdependencies simulation platform for disaster response support. DR-NEP is a simulation network platform that integrates different simulators for different infrastructures to form a universal simulation platform. We employ a new concept in Discrete Event Systems optimization called Ordinal Optimization to address the problem of resources allocation during a disaster event. The objective of the optimization problem is maximizing the operational capacity of a critical infrastructure, a hospital in this case. Due to the huge combinatorial feasible search space, an Ordinal Optimization based approach is used to solve the problem using two main concepts: goal softening and order comparison. This approach aims at finding a Good Enough solution set (G) with an acceptable probability and efficient computational effort. This paper describes early results of our work that shows the use of our approach in optimizing resources allocation in a simulated disaster event.