Abstract-Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures of their physical infrastructure, resulting from physical attacks (such as an Electromagnetic Pulse attack) or natural disasters (such as solar flares, earthquakes, and floods). Such events happen at specific geographical locations and disrupt specific parts of the network but their effects are not deterministic. Therefore, we provide a unified framework to model the network vulnerability when the event has a probabilistic nature, defined by an arbitrary probability density function. Our framework captures scenarios with a number of simultaneous attacks, in which network components consist of several dependent subcomponents, and in which either a 1+1 or a 1:1 protection plan is in place. We use computational geometric tools to provide efficient algorithms to identify vulnerable points within the network under various metrics. Then, we obtain numerical results for specific backbone networks, thereby demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows for identifying locations which require additional protection efforts (e.g., equipment shielding). Overall, the paper demonstrates that using computational geometric techniques can significantly contribute to our understanding of network resilience.
Abstract-Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures of their physical infrastructure, resulting from physical attacks (such as an Electromagnetic Pulse attack) or natural disasters (such as solar flares, earthquakes, and floods). Such events happen at specific geographical locations and disrupt specific parts of the network but their effects are not deterministic. Therefore, we provide a unified framework to model the network vulnerability when the event has a probabilistic nature, defined by an arbitrary probability density function. Our framework captures scenarios with a number of simultaneous attacks, in which network components consist of several dependent subcomponents, and in which either a 1+1 or a 1:1 protection plan is in place. We use computational geometric tools to provide efficient algorithms to identify vulnerable points within the network under various metrics. Then, we obtain numerical results for specific backbone networks, thereby demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows for identifying locations which require additional protection efforts (e.g., equipment shielding). Overall, the paper demonstrates that using computational geometric techniques can significantly contribute to our understanding of network resilience.
Abstract-Telecommunications networks heavily rely on the physical infrastructure and, are therefore, vulnerable to natural disasters, such as earthquakes or floods, as well as to physical attacks, such as an Electromagnetic Pulse (EMP) attack. Largescale disasters are likely to destroy network equipment and to severely affect interdependent systems such as the power-grid. In turn, long-term outage of the power-grid might cause additional failures to the telecommunication network.In this paper, we model an attack as a disk around its epicenter, and provide efficient algorithms to find vulnerable points within the network, under various metrics. In addition, we consider the case in which multiple disasters happen simultaneously and provide an approximation algorithm to find the points which cause the most significant destruction. Finally, since a network element does not always fail, even when it is close to the attack's epicenter, we consider a simple probabilistic model in which the probability of a network element failure is given. Under this model, we tackle the cases of single and multiple attacks and develop algorithms that identify potential points where an attack is likely to cause a significant damage.Index Terms-Network survivability, geographic networks, network design, Electromagnetic Pulse (EMP), computational geometry.
The growth in knowledge sharing enabled by the (Semantic)
Abstract. We study the problem of covering a two-dimensional spatial region P , cluttered with occluders, by sensors. A sensor placed at a location p covers a point x in P if x lies within sensing radius r from p and x is visible from p, i.e., the segment px does not intersect any occluder. The goal is to compute a placement of the minimum number of sensors that cover P . We propose a landmark-based approach for covering P . Suppose P has ς holes, and it can be covered by h sensors. Given a small parameter ε > 0, let λ := λ(h, ε) = (h/ε) log ς. We prove that one can compute a set L of O(λ log λ log (1/ε)) landmarks so that if a set S of sensors covers L, then S covers at least (1 − ε)-fraction of P . It is surprising that so few landmarks are needed, and that the number does not depend on the number of vertices in P . We then present efficient randomized algorithms, based on the greedy approach, that, with high probability, compute O(h log λ) sensor locations to cover L; hereh ≤ h is the number sensors needed to cover L. We propose various extensions of our approach, including: (i) a weight function over P is given and S should cover at least (1 − ε) of the weighted area of P , and (ii) each point of P is covered by at least t sensors, for a given parameter t ≥ 1.
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