Gabriele Oliva

Agent-based input–output interdependency model

The modeling and analysis of critical infrastructures and their interdependencies are essential to discovering hidden vulnerabilities and the related threats to national and international security. Over the past few years, several approaches have been proposed to address this problem. The so-called holistic approaches are relatively abstract, but are easily validated using real economic data. Other approaches based on agent-based models provide deeper views of the interdependencies existing between subsystems of different infrastructures. However, agent-based models are often difficult to validate because quantitative data of the appropriate granularity may not be available. This paper presents an agent-based input–output inoperability model designed to overcome the limitations of the holistic and agent-based paradigms. In order to provide a detailed and expressive framework, the exchange of resources between infrastructures is explicitly modeled while inoperability becomes an internal parameter. Nevertheless, the model is easily transformed into a fine-grained, input–output inoperability model whose coefficients can be obtained based on real data.

Fuzzy dynamic input–output inoperability model

Abstract This paper describes an extension of the input–output inoperability model (IIM) that accommodates uncertain and vague data. In the resulting “fuzzy version” of the dynamic IIM model (FD-IIM), the level of inoperability of each infrastructure and the Leontief coefficients are specified using fuzzy variables that express expert knowledge about infrastructure interdependences. An important result is that, under certain conditions, solution convergence for the fuzzy model can be inferred based on the stability properties of the “crisp” (non-fuzzy) version of the system of interest. A case study involving the Italian critical infrastructure is used to demonstrate the feasibility and utility of the approach.

Improving Resilience of Interdependent Critical Infrastructures via an On-Line Alerting System

This paper illustrates the activities under development within the FP7 EU MICIE project. The project is devotedto design and implement an on-line alerting system, able toevaluate, in real time, the level of risk of interdependent Critical Infrastructures (CIs). Such a risk is generated by undesired events and by the high level of interconnection of the different infrastructures. Heterogeneous models are under development to perform short term predictions of the Quality of Service (QoS) of each CI according to the QoS of the others, to the level of interdependency among the Infrastructures, and according to the undesired events identified in the reference scenario.

Distributed synchronization under uncertainty: A fuzzy approach

In this paper the synchronization of a network of identical systems with fuzzy initial conditions is introduced, as a convenient framework to obtain a shared estimation of the state of a system based on partial and distributed observations, in the case where such a state is affected by ambiguity and/or vagueness. After discussing the synchronization of crisp systems, providing a criteria to find the information sharing law that lets the network converge to a shared trajectory, Discrete-Time Fuzzy Systems (DFSs) are introduced as an extension of scalar fuzzy difference equations. Besides providing a stability condition for a general DFS, in the linear case it is proven that, under a non-negativity assumption for the coefficients of the system, the fuzzyfication of the initial conditions does not compromise the stability of the crisp system. A framework for the synchronization of arrays of linear DFS is then introduced, proving that the crisp synchronization is a particular case of the proposed approach. Finally, a case study in the field of Critical Infrastructure Protection is provided.

A HOLISTIC-REDUCTIONISTIC APPROACH FOR MODELING INTERDEPENDENCIES

Modeling and analyzing critical infrastructures and their interdependencies are essential to discovering hidden vulnerabilities and threats. Several current approaches engage a holistic perspective and rely on abstract models; others incorporate a reductionistic perspective and focus on inter-domain and intra-domain interactions among elementary components. This paper proposes a mixed approach in which holism and reductionism coexist. A critical infrastructure is expressed at different, albeit interrelated, levels of abstraction, and intermediate entities that provide specific aggregate resources or services are introduced.

Sensor Networks Localization: Extending Trilateration via Shadow Edges

Distance-based network localization is known to have solution, in general, if the network is globally rigid. In this technical note we relax this condition with reference to unit disk graphs. To this end, shadow edges are introduced to model the fact that selected nodes are not able to sense each other. We provide a localization algorithm based on such edges and a necessary and sufficient localizability condition. We also investigate the relation between the proposed approach and trilateration, showing from both a theoretical and empirical perspective that shadow edge localization succeeds also when trilateration fails.

On the distributed synchronization of on-line IIM interdependency models

In the last few years Critical Infrastructures have become more and more tightly interconnected and their protection is one of the major issues for the national and international security. In order to achieve that, many modeling techniques for the interdependencies existing among them have been proposed. Now, a crucial issue is how to use such models to develop tools able to estimate the status of key elements of CIs, quantify the possible threats and suggest adequate countermeasures to human operators and actors. Due to security, commercial and technological aspects, the only feasible approach is to provide distributed and interconnected state/interdependency estimators. In this paper the general problem of the state estimation of interconnected systems sharing the same model is introduced. A first step in the solution of such a challenging problem is then provided in the case of linear systems. The final objective of this research is to define an effective framework for the problem at hand, and then implement and validate an on-line distributed state/interdependency estimator within the EU IST MICIE project.

Simultaneous Localization and Routing in Sensor Networks using Shadow Edges

Abstract In the last decade, Wireless Sensor Networks (WSN) have been adopted in a wide range of industrial and consumer applications to perform various monitoring tasks such as search, rescue, disaster relief, target tracking and smart environments control. In many such tasks, node localization is inherently one of the system parameters. Node localization is required to gain spatial awareness of the supervised area. In this work a simultaneous localization and routing algorithm is proposed. The localization is obtained by a ranging technique, exploiting network topology provided by the routing algorithm to reduce the network signaling communication, which is the most power-consuming operation in WSN, as much as possible. To this end, Shadow Edges, a novel class of links, is considered to take into account the lack of communication among nodes.

Fuzzy Importance Measures for Ranking Key Interdependent Sectors Under Uncertainty

In the field of reliability engineering, several approaches have been developed to identify those components that are important to the operation of the larger interconnected system. We extend the concept of component importance measures to the study of industry criticality in a larger system of economically interdependent industry sectors that are perturbed when underlying infrastructures are disrupted. We provide measures of (i) those industries that are most vulnerable to disruptions and (ii) those industries that are most influential to cause interdependent disruptions. However, difficulties arise in the identification of critical industries when uncertainties exist in describing the relationships among sectors. This work adopts fuzzy measures to develop criticality indices, and we offer an approach to rank industries according to these fuzzy indices. Much like decision makers with the knowledge of the most critical components in a physical system, the identification of these critical industries provides decision makers with priorities for resources. We illustrate our approach with an interdependency model driven by US Bureau of Economic Analysis data to describe industry interconnectedness.

Exploiting routing information in Wireless Sensor Networks localization

In this work, an integrated solution to jointly solve the localization and data routing problems in sensor networks is proposed. It is based on a scalable “divide et impera” approach. The network is divided into 1-hop cluster and the localization is hierarchically performed inside each cluster, thereafter among clusters. The key advantage of the algorithm is that no additional communication is required to perform localization, which is obtained by a ranging technique, exploiting network topology provided by the routing algorithm. To this end, Shadow Edges, a novel class of links, are introduced to encompass the lack of communication among nodes. The proposed dual procedure to solve the localization and routing, represents a novel trend for Wireless Sensor Networks, where multiple problems are jointly solved by providing affordable and integrated solutions.