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2014 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2014

作者： Sen, Arunabha Mazumder, Anisha Banerjee, Joydeep Das, Arun Compton, Randy Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe AZ 85287 United States

ISBN: (纸本)9781479930883

The critical infrastructures of a nation including the power grid and the communication network are highly interdependent. Recognizing the need for a deeper understanding of the interdependency in a multi-layered network, significant efforts have been made by the research community in the last few years to achieve this goal. Accordingly a number of models have been proposed and analyzed. Unfortunately, most of the models are over simplified and, as such, they fail to capture the complex interdependency that exists between entities of the power grid and the communication networks involving a combination of conjunctive and disjunctive relations. To overcome the limitations of existing models, we propose a new model that is able to capture such complex interdependency relations. Utilizing this model, we provide techniques to identify the K most 'vulnerable' nodes of an interdependent network. We show that the problem can be solved in polynomial time in some special cases, whereas for some others, the problem is NP-complete. We establish that this problem is equivalent to computation of a fixed point of a multilayered network system and we provide a technique for its computation utilizing Integer Linear programming. Finally, we evaluate the efficacy of our technique using real data collected from the power grid and the communication network that span the Maricopa County of Arizona. © 2014 IEEE.

关键词： Complex networks

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Identification of K most vulnerable nodes in multi-layered network using a new model of interdependency

Identification of K most vulnerable nodes in multi-layered n...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Arunabha Sen Anisha Mazumder Joydeep Banerjee Arun Das Randy Compton Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona

The critical infrastructures of a nation including the power grid and the communication network are highly interdependent. Recognizing the need for a deeper understanding of the interdependency in a multi-layered network, significant efforts have been made by the research community in the last few years to achieve this goal. Accordingly a number of models have been proposed and analyzed. Unfortunately, most of the models are over simplified and, as such, they fail to capture the complex interdependency that exists between entities of the power grid and the communication networks involving a combination of conjunctive and disjunctive relations. To overcome the limitations of existing models, we propose a new model that is able to capture such complex interdependency relations. Utilizing this model, we provide techniques to identify the K most “vulnerable” nodes of an interdependent network. We show that the problem can be solved in polynomial time in some special cases, whereas for some others, the problem is NP-complete. We establish that this problem is equivalent to computation of a fixed point of a multilayered network system and we provide a technique for its computation utilizing Integer Linear programming. Finally, we evaluate the efficacy of our technique using real data collected from the power grid and the communication network that span the Maricopa County of Arizona.

关键词： Equations Communication networks Mathematical model Power grids Power system faults Power system protection Nonhomogeneous media

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Root Cause Analysis of Failures in Interdependent Power-Communication Networks

Root Cause Analysis of Failures in Interdependent Power-Comm...

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MILCOM, Military Communications Conference

作者： Arun Das Joydeep Banerjee Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona

ISBN: (纸本)9781479967711

Studies on communication network robustness, though extensive, have primarily focused on standalone networks. However, real world communication networks do not operate in isolation, but instead are part of complex interdependent ecosystems that function together as a comprehensive symbiotic unit. For instance, communication networks of today are highly dependent on the power infrastructure and hence, any efforts to improve the robustness of communication networks must necessarily take into account the vulnerabilities of the power infrastructure and their shared interdependencies. For example, event-induced failures (failures caused by natural disasters or terrorist attacks), on any of these two infrastructures can trigger further failures (triggered failures) into the system through a cascading process due to the interdependencies between the networks. In such an interdependent system where an event induced failure can cascade and result in a much larger combined (event-induced plus triggered) failure, it may be imperative to identify the original event-induced failure for the purpose of post fault diagnostics, or for pre-cascade network strengthening. This ascertainment of event-induced original failure, or the Root Cause of Failure, from combined failures in interdependent Power-Communication networks is the main focus of this paper. We model interdependencies between the two infrastructures using the recently proposed Implicative Interdependency Model. We introduce the Root Cause of Failure problem, and prove it to be NP-Complete. We also provide optimal solutions using ILP, and provide an O(ln(n)) approximation algorithm. Finally, we validate our analytical results through experiments in the power communication network of Maricopa County, Arizona.

关键词： Communication networks Silicon Approximation algorithms Computational modeling Power system faults Power system protection Approximation methods

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On the impact of coding parameters on storage requirement of region-based fault tolerant distributed file system design

On the impact of coding parameters on storage requirement of...

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International Conference on computing, Networking and Communications (ICNC)

作者： Sujogya Banerjee Arun Das Anisha Mazumder Zahra Derakhshandeh Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona

Advances in technology have resulted in Internet-scale deployment of storage systems such as peer-to-peer storage and cloud storage, where data is distributed over multiple storage nodes in a networked environment. In these environments the storage nodes are often commodity machines and are susceptible to failure. The notion of fault domain, introduced by Microsoft Azure, captures the fault-tolerance aspects of a data center. A fault domain is defined as a set of servers all of which become inaccessible when a single fault (such as the failure of a switch or a router) occurs in the data center. As such a fault domain can be viewed as a spatially correlated or region based failure. In order to enhance reliability through redundancy, maximum distance separable (MDS) codes such as Reed-Solomon codes and (N, K) codings are utilized. In this paper we present analytical results demonstrating that the choice of the coding parameters N and K may have significant impact on storage that will be necessary to achieve reliability. We present a polynomial time algorithm for optimal storage allocation in a mesh network and we conduct extensive experimentation to evaluate the impact of the coding parameters N and K on the storage requirement to provide all region fault tolerance with varying size of the mesh and the fault region.

关键词： Color Image color analysis Encoding Silicon Peer-to-peer computing Fault tolerant systems Fault tolerance

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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On region-based fault tolerant design of distributed file storage in networks

On region-based fault tolerant design of distributed file st...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Sujogya Banerjee Shahrzad Shirazipourazad Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe AZ USA

ISBN: (纸本)9781467307734;9781467307758

Distributed storage of data files in different nodes of a network enhances the reliability of the data by offering protection against node failure. In the (N,K),N ≥ K file distribution scheme, from a file F of size |F|, N segments of size |F|/K are created in such a way that it is possible to reconstruct the entire file, just by accessing any K segments. For the reconstruction scheme to work it is essential that the K segments of the file are stored in nodes that are connected in the network. However in case of node failures the network might become disconnected (i.e., split into several connected components). We focus on node failures that are spatially-correlated or region-based. Such failures are often encountered in disaster situations or natural calamities where only the nodes in the disaster zone are affected. The goal of this research is to devise a file segment distribution scheme so that, even if the network becomes disconnected due to any region fault, at least one of the largest connected components will have at least K distinct file segments with which to reconstruct the entire file. The distribution scheme will also ensure that the total storage requirement is minimized. We provide an optimal solution through Integer Linear programming and an approximation solution with a guaranteed performance bound of O(ln n) to solve the problem for any arbitrary network. The performance of the approximation algorithm is evaluated by simulation on two real networks.

关键词： Image color analysis Color Approximation algorithms Robustness Approximation methods Layout Encoding

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The Single Vehicle Routing Problem with Deliveries and Selective Pickups in a CPU-GPU Heterogeneous Environment

The Single Vehicle Routing Problem with Deliveries and Selec...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Igor Machado Coelho Luiz Satoru Ochi Pablo Luiz Araújo Munhoz Marcone Jamilson Freitas Souza Ricardo Farias Cristiana Bentes Institute of Computing Fluminense Federal University UFF Niteroi Brazil Department of Computer Science UFOP-Federal University of Ouro Preto and FEAD Ouro Preto Brazil System Engineering Program-COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil Department of System Engineering State University of Rio de Janeiro Rio de Janeiro Brazil

In this work, we propose a new algorithm to solve a variant of the Vehicle Routing Problem that is the Single Vehicle Routing Problem with Deliveries and Selective Pickups (SVRPDSP). Our algorithm produces good quality solutions that are better than the best known solutions in the literature. In order to reduce the time spent to solve large-sized instances, we also propose here a parallel implementation of our algorithm that explores a heterogeneous environment composed of a CPU and a GPU. Therefore, our algorithm harnesses the tremendous computing power of the GPU to improve the performance of the local searches computation. We obtained average speedups from 2.73 to 16.23 times with our parallel approach.

关键词： Graphics processing unit Vehicles Parallel algorithms Vectors Resource management Optimization Routing

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Beyond connectivity-new metrics to evaluate robustness of networks

Beyond connectivity-new metrics to evaluate robustness of ne...

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International Conference on High Performance Switching and Routing

作者： Banerjee, Sujogya Shirazipourazad, Shahrzad Ghosh, Pavel Sen, Arunabha Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe AZ 85281 United States

ISBN: (纸本)9781424484560

Robustness or fault-tolerance capability of a network is an important design parameter in both wired and wireless networks. Connectivity of a network is traditionally considered to be the primary metric for evaluation of its fault-tolerance capability. However, connectivity (G) (for random faults) or region-based connectivity R(G) (for spatially correlated or region-based faults, where the faults are confined to a region R) of a network G, does not provide any information about the network state, (i.e., whether the network is connected or not) once the number of faults exceeds (G) or R(G). If the number of faults exceeds (G) or R(G), one would like to know, (i) the number of connected components into which G decomposes, (ii) the size of the largest connected component, (iii) the size of the smallest connected component. In this paper, we introduce a set of new metrics that computes these values. We focus on one particular metric called region-based component decomposition number (RBCDN), that measures the number of connected components in which the network decomposes once all the nodes of a region fail. We study the computational complexity of finding RBCDN of a network. In addition, we study the problem of least cost design of a network with a target value of RBCDN. We show that the optimal design problem is NP-complete and present an approximation algorithm with a performance bound of O(log K + 4log n), where n denotes the number of nodes in the graph and K denotes a target value of RBCDN. We evaluate the performance of our algorithm by comparing it with the performance of the optimal solution. Experimental results demonstrate that our algorithm produces near optimal solution in a fraction of time needed to find an optimal solution. © 2011 IEEE.

关键词： Approximation algorithms

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On connectivity of Airborne Networks in presence of region-based faults

On connectivity of Airborne Networks in presence of region-b...

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MILCOM, Military Communications Conference

作者： Shahrzad Shirazipourazad Pavel Ghosh Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University USA

The U.S. Air Force is currently in the process of building an Airborne Network (AN), where the nodes are a set of heterogeneous, highly mobile, Airborne Networking Platforms (ANPs) - such as satellites, airplanes and unmanned aerial vehicles. Mobility pattern of nodes in a mobile network has significant impact on the coverage and connectivity properties of the network. The level of reliability needed for continuous operation of an AN may be difficult to achieve through a completely infrastructure-less mobile ad hoc networks. In an earlier paper, we proposed an architecture for an AN where a set of ANPs form the backbone of the AN. In this architecture, the ANPs may be viewed as mobile base stations with predictable and well-structured flight paths and the combat aircrafts on a mission as mobile clients. In this paper we consider the AN scenario where a part of the network might not be operational due to enemy attack and/or jamming. We consider faults that are spatially correlated (or region-based), that is faults due to an enemy attack are confined to a region. The goal is to design a robust AN so that no matter which region in the deployment area fails and at what time, the surviving nodes of the network will remain connected and be able to communicate with each other. We propose an algorithm that finds the minimum transmission range necessary to ensure network connectivity irrespective of location of the fault region and the time of the failure.

关键词： Mobile communication Computer architecture Mobile computing Reliability Orbits Aircraft Network topology

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