The wireless network jamming problem

Commander, Clayton W.; Pardalos, Pãnos M.; Ryabchenko, Valeriy; Uryasev, Stan; Zrazhevsky, Grigoriy

doi:10.1007/s10878-007-9071-7

Cited by 60 publications

(63 citation statements)

References 11 publications

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“…Percentile risk constraints will be incorporated into the formulation. Commonly applied in financial applications, risk measures such as Value-at-Risk (VaR) and Conditional Valueat-Risk have proven to be effective tools for military applications as well [8,11,12]. We also plan to provide some theoretical results regarding feasibility of problem instances.…”

Section: Resultsmentioning

confidence: 99%

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Communication Models for a Cooperative Network of Autonomous Agents

Arulselvan

Commander²,

Hirsch

2009

Springer Optimization and Its Applications

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ABSTRACT. We consider the problem of maximizing the total connectivity for a set of wireless agents in a mobile ad hoc network. That is, given a set of wireless units each having a start point and a destination point, our goal is to determine a set of routes for the units which maximizes the overall connection time between them. Known as the COOPERATIVE COMMUNICATION PROBLEM IN MOBILE AD HOC NETWORKS (CCPM), this problem has several military applications including coordination of rescue groups, path planning for unmanned air vehicles, and geographical exploration and target recognition. The CCPM is N P-hard; therefore heuristic development has been the major focus of research. In this work, we survey the CCPM examining first some early combinatorial formulations and solution techniques. Then we introduce new continuous formulations and compare the results of several case studies. By removing the underlying graph structure, we are able to create a more realistic model of the problem as supported by the numerical evidence.

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Section: Resultsmentioning

confidence: 99%

“…Specific examples include combat search and rescue teams, and medical teams. In the wake of disasters such as the terrorist attacks of September 11,2001, and Hurricane Katrina, the nation saw first hand that communication among the emergency responders was critical to the success of the rescue operations.…”

Section: Introductionmentioning

confidence: 99%

Communication Models for a Cooperative Network of Autonomous Agents

Arulselvan

Commander²,

Hirsch

2009

Springer Optimization and Its Applications

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“…In economic terms, VaR is simply the maximum amount at risk to be lost from an investment. VaR is the most widely applied risk measure in probabilistic settings primarily because it is conceptually simple and easy to incorporate into a mathematical model [6]. However with this ease of use is accompanied by several complicating factors.…”

Section: Statistical Measures Of Losses For Optimization Problems Undmentioning

confidence: 99%

“…Suppose that the random variable L(x, y) (referred to as a loss function) represents a total collateral loss associated with a decision vector x ∈ X ⊆ R n , and a random vector y ∈ R m which represents the uncertain parameters that may affect the performance of a system under consideration. Assume that y is governed by a probability measure P on a Borel set, say Y [6]. Then the loss L(x, y) for each x ∈ X is a random variable having a distribution in R induced by that of y.…”

Section: Lossmentioning

confidence: 99%

Polynomial-time identification of robust network flows under uncertain arc failures

2009

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ABSTRACT. We propose LP-based solution methods for network flow problems subject to multiple uncertain arc failures, which allow finding robust optimal solutions in polynomial time under certain conditions. We justify this fact by proving that for the considered class of problems under uncertainty with linear loss functions, the number of entities in the corresponding LP formulations is polynomial with respect to the number of arcs in the network. The proposed formulation is efficient for sparse networks, as well as for timecritical networked systems, where quick and robust decisions play a crucial role.

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“…Stability becomes especially important in applications, where perturbations almost always need to be considered due to incomplete distributional information about a random variable and other approximations. Superquantiles are therefore widely used in financial engineering (Yamai and Yoshiba [41], Alexander et al [2], Wang and Uryasev [39], Balbas et al [5], Uryasev et al [37]), but increasingly also in other application areas such as structural engineering (Rockafellar and Royset [27], Minguez et al [20], Haukaas et al [15]), military operations (Commander et al [8], Kalinchenko et al [16], Molyboha and Zabarankin [21]), natural resources (Yamout et al [42], Webby et al [40]), supply chains (Tomlin [36], Verderame and Floudas [38]), and energy systems (Carrion et al [7], Conejo et al [9]), to mention a few. We refer to Rockafellar [26], Sarykalin et al [35], and Krokhmal et al [19] for earlier reviews of risk measures and superquantiles, as well as for a more comprehensive list of references.…”

Section: Introductionmentioning

confidence: 99%

Superquantiles and Their Applications to Risk, Random Variables, and Regression

Rockafellar

Røyset

2013

Theory Driven by Influential Applications

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Please scroll down for article-it is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Abstract Superquantiles (also called conditional values-at-risk) are useful tools in risk modeling and optimization, with expanding roles beyond these areas. This tutorial provides a broad overview of superquantiles and their versatile applications. We see that superquantiles are as fundamental to the description of a random variable as the cumulative distribution function (cdf), they can recover the corresponding quantile function through differentiation, they are dual in some sense to superexpectations, which are convex functions uniquely defining the cdf, and they also characterize convergence in distribution. A superdistribution function defined by superquantiles leads to higher-order superquantiles as well as new measures of risk and error, with important applications in risk modeling and generalized regression.

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The wireless network jamming problem

Cited by 60 publications

References 11 publications

Communication Models for a Cooperative Network of Autonomous Agents

Communication Models for a Cooperative Network of Autonomous Agents

Polynomial-time identification of robust network flows under uncertain arc failures

Superquantiles and Their Applications to Risk, Random Variables, and Regression

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