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SelectCast: Scalable Data Aggregation Scheme in Wireless Sensor Networks

IEEE Transactions on Parallel and Distributed Systems 2012年第10期23卷 1958-1969页

作者： Cheng Wang Changjun Jiang Shaojie Tang Xiang-Yang Li Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science and Engineering Tsinghua National Laboratory for Information Science and Technology (TNLIST) University of Tongji China

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density \lambda \in [1,n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SHL) scheme and multiple-hop-length (MHL) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MHL scheme, for a practically important set of symmetric functions called divisible perfectly compressible (DPC) functions, including the mean, max, and various kinds of indicator functions, etc., the data from \Theta (n) sensors can be aggregated to the sink at the throughput of a constant order \Theta (1), implying that, our MHL scheme is indeed scalable.

关键词： Throughput Wireless sensor networks Temperature measurement Protocols Lattices Temperature sensors

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Multicast Capacity Scaling Laws for Multihop Cognitive Networks

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IEEE Transactions on Mobile computing 2012年第11期11卷 1627-1639页

作者： Cheng Wang Shaojie Tang Xiang-Yang Li Changjun Jiang Key Laboratory of Embedded System and Service Computing Building of Electronics and Information Engineering Ministry of Education Shanghai China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Illinois Institute of Technology Chicago IL USA Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering University of Tongji Shanghai China

In this paper, we study multicast capacity for cognitive networks. We consider the cognitive network model consisting of two overlapping ad hoc networks, called the primary ad hoc network (PaN) and secondary ad hoc network (SaN), respectively. PaN and SaN operate on the same space and spectrum. For PaN (or SaN, respectively), we assume that primary (or secondary, respectively) nodes are placed according to a Poisson point process of intensity n (or m, respectively) over a unit square region. We randomly choose n s (or m s , respectively) nodes as the sources of multicast sessions in PaN (or SaN, respectively), and for each primary source vp (or secondary source v s , respectively), we pick uniformly at random nd primary nodes (or md secondary nodes, respectively) as the destinations of v p (or v s , respectively). Above all, we assume that PaN can adopt the optimal protocol in terms of the throughput. Our main work is to design the multicast strategy for SaN by which the optimal throughput can be achieved, without any negative impact on the throughput for PaN in order sense. Depending on nd and n, we choose the optimal one for PaN from two strategies called percolation strategy and connectivity strategy, respectively. Subsequently, we design the corresponding throughput-optimal strategy for SaN. We derive the regimes in terms of n, n d , m, and m d in which the upper bounds on multicast capacities for PaN and SaN can be achieved simultaneously. Unicast and broadcast capacities for the cognitive network can be derived by our results as the special cases by letting n d = 1 (or m d = 1) and n d = n - 1 (or m d = m - 1), respectively, which enhances the generality of this work.

关键词： Throughput Ad hoc networks Road transportation Slabs Routing Lattices Mobile computing

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Does independent component analysis perform well for iris recognition?

Does independent component analysis perform well for iris re...

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作者： Bouraoui, Imane Chitroub, Salim Bouridane, Ahmed Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Science and Technology of Houari Boumedienne P.O. Box 32 El - Alia Bab - Ezzouar 16111 Algiers Algeria School of Computing Engineering and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom Department of Computer Science King Saud University Riyadh Saudi Arabia

This paper is concerned with an application of ICA for a possible improvement of iris recognition by replying to the question: does ICA perform well for such purpose? To achieve this, the hypotheses and the theoretical concepts of ICA methods used are handled so that coherence with iris authentication application is guaranteed. Our contribution is not in the development of new theoretical concepts of ICA but it is in adapting its basic ideas for our application. Also, it consists of deploying its powerful and its efficiency for iris recognition, and consequently;it's potential to embed it on smart cards for increasing application domains of secure biometric-based individual identification systems. We have developed a comparative study between the implemented ICA algorithms and other recent and popular methods of iris recognition. We have demonstrated our experimental results using some mathematical criteria. Three different subsets of international certified CASIA iris image databases are used for testing the different implemented methods. The conclusion of such comparative study is that the ICA-based approaches are more effective and more practical than other existing methods. © 2012 - IOS Press and the authors. All rights reserved.

关键词： Biometrics

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Erratum: Search for new phenomena in final states with large jet multiplicities and missing transverse momentum at = 8 TeV proton-proton collisions using the ATLAS experiment

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Journal of High Energy Physics 2014年第1期2014卷 1-18页

作者： G. Aad T. Abajyan B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams T. N. Addy J. Adelman S. Adomeit T. Adye S. Aefsky T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen A. Ahmad M. Ahsan G. Aielli T. P. A. Åkesson G. Akimoto A. V. Akimov M. A. Alam J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov F. Alessandria C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob M. Aliev G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport S. E. Allwood-Spiers J. Almond A. Aloisio R. Alon A. Alonso F. Alonso A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung V. V. Ammosov S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi A. Antonaki M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella R. Apolle G. Arabidze I. Aracena Y. Arai A. T. H. Arce S. Arfaoui J.-F. Arguin S. Argyropoulos E. Arik M. Arik A. J. Armbruster O. Arnaez V. Arnal O. Arslan A. Artamonov G. Artoni D. Arutinov S. Asai N. Asbah S. Ask B. Åsman L. Asquith K. Assamagan R. Astalos A. Astbury M. Atkinson N. B. Atlay B. Auerbach E. Auge K. Augsten M. Aurousseau G. Avolio D. Axen G. Azuelos Y. Azuma M. A. Baak C. Bacci A. M. Bach H. Bachacou K. Bachas M. Backes M. Backhaus J. Backus Mayes E. Badescu P. Bagiacchi P. Bagnaia Y. Bai D. C. Bailey T. Bain J. T. Baines O. K. Baker S. Baker P. Balek F. Balli E. Banas Sw. Banerjee D. Banfi A. Bangert V. Bansal H. S. Bansil L. Barak S. P. Baranov T. Barber E. L. Barberio D. Barberis M. Barbero D. Y. Bardin T. Barillari M. Barisonzi T. Barklow N. Barlow B. M. Barnett R. M. Barnett A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton V. Bartsch A. B Fakultät für Mathematik und Physik Albert-Ludwigs-Universität Freiburg Germany Physikalisches Institut University of Bonn Bonn Germany Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institut de Física d’Altes Energies and Departament de Física de la Universitat Autònoma de Barcelona Barcelona Spain LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam Netherlands Department of Physics Oklahoma State University Stillwater United States of America Department of Physics and Astronomy Michigan State University East Lansing United States of America Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel DSM/IRFU (Institut de Recherches sur les Lois Fondamentales de l’Univers) CEA Saclay (Commissariat à l’Energie Atomique et aux Energies Alternatives) Gif-sur-Yvette France Department of Physics Stockholm University Stockholm Sweden INFN Gruppo Collegato di Udine Udine Italy Department of Physics King’s College London London United Kingdom AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Physics Department Brookhaven National Laboratory Upton United States of America Department of Physics Hampton University Hampton United States of America Department of Physics Yale University New Haven United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Department of Physics Brandeis University Waltham United States of America Vinca Institute of Nuclear Sciences University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP Lisboa Portug

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Cloud Resource Provisioning to Extend the Capacity of Local Resources in the Presence of Failures

Cloud Resource Provisioning to Extend the Capacity of Local ...

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

作者： Bahman Javadi Parimala Thulasiraman Rajkumar Buyya School of Computing Engineering and Mathematics University of Western Ontario Australia IDEAS Laboratory Department of Computer Science University of Manitoba Canada CLOUDS Laboratory Department of Computing and Information Systems University of Melbourne Australia

In this paper, we investigate Cloud computing resource provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate check pointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning policies that can be utilized by an organization that operates a cluster managed by virtual machine technology and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users' requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud.

关键词： Cloud computing Mathematical model Routing Equations Time factors Scheduling algorithms Virtual machining

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Integrating nature-inspired optimization algorithms to K-means clustering

Integrating nature-inspired optimization algorithms to K-mea...

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International Conference on Digital information Management (ICDIM)

作者： Rui Tang Simon Fong Xin-She Yang Suash Deb Department of Computer and Information Science SAR Macau Mathematics and Scientific Computing National Physical Laboratory Teddington UK Department of Computer Science & Engineering C. V. Raman College of Engineering Bidyanagar India

Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization algorithms inspired by biology, including the Wolf, Firefly, Cuckoo, Bat and Ant algorithms, simulate swarm behavior in which peers are attracted while steering towards a global objective. It is found that these bio-inspired algorithms have their own virtues and could be logically integrated into K-means clustering to avoid local optima during iteration to convergence. In this paper, the constructs of the integration of bio-inspired optimization methods into K-means clustering are presented. The extended versions of clustering algorithms integrated with bio-inspired optimization methods produce improved results. Experiments are conducted to validate the benefits of the proposed approach.

关键词： Clustering algorithms Algorithm design and analysis Flowcharts Partitioning algorithms Optimization methods Heuristic algorithms

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Scaling Laws of Multicast Capacity for Power-Constrained Wireless Networks under Gaussian Channel Model

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IEEE Transactions on computers 2012年第5期61卷 713-725页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Shaojie Tang Yuan He Xufei Mao Yunhao Liu Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering Building of Electronics and Information Engineering University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA Tsinghua National Laboratory for Information Science and Technology (TNLIST) Tsinghua University Beijing China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Hong Kong University of Science and Technology Kowloon Hong Kong China Tsinghua National Laboratory for Information Science and Technology (TNLIST) School of Software Tsinghua University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

We study the asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent α >; 2. In REN, n nodes are randomly distributed in the square region of side length √n. There are n s randomly and independently chosen multicast sessions. Each multicast session has n d + 1 randomly chosen terminals, including one source and n d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n s = ω(1) and nd. As a special case of our results, we show that for n s = Θ(n), the per-session multicast capacity for REN is of order Θ(1/√n d n) when nd = O(n/(log n) a+1 ) and is of order Θ(1/n d · (log n) -n/2 ) when n d = Ω(n/log n).

关键词： Lattices Throughput Upper bound Road transportation Receivers Interference Routing

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Nature-Inspired Clustering Algorithms for web Intelligence Data

Nature-Inspired Clustering Algorithms for Web Intelligence D...

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IEEE WIC ACM International Conference on web Intelligence (WI)

作者： Tang Rui Simon Fong Xin-She Yang Suash Deb Department of Computer and Information Science University of Macau Macao China Mathematics and Scientific Computing National Physical Laboratory London UK Department of Computer Science & Engineering C. V. Raman College of Engineering Bhubaneswar India

Clustering algorithms are an important component of data mining technology which has been applied widely in many applications including those that operate on Internet. Recently a new line of research namely web Intelligence emerged that demands for advanced analytics and machine learning algorithms for supporting knowledge discovery mainly in the web environment. The so called web Intelligence data are known to be dynamic, loosely structured and consists of complex attributes. To deal with this challenge standard clustering algorithms are improved and evolved with optimization ability by swarm intelligence which is a branch of nature-inspired computing. Some examples are PSO Clustering (C-PSO) and Clustering with Ant Colony Optimization. The objective of this paper is to investigate the possibilities of applying other nature-inspired optimization algorithms (such as Fireflies, Cuckoos, Bats and Wolves) for performing clustering over web Intelligence data. The efficacies of each new clustering algorithm are reported in this paper, and in general they outperformed C-PSO.

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A segmentation and data association annotation system for laser-based multi-target tracking evaluation

A segmentation and data association annotation system for la...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Chien-Chen Weng Chieh-Chih Wang Jennifer Healey Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering Graduate Institute of Networking and Multimedia and the Intel-NTU Connected Context Computing Center National Taiwan University Taipei Taiwan INTEL Research Laboratory Santa Clara CA USA

2D laser scanners are now widely used to accomplish robot perception tasks such as SLAM and multi-target tracking (MTT). While a number of SLAM benchmarking datasets are available, only a few works have discussed the issues of collecting multi-target tracking benchmarking datasets. In this work, a segmentation and data association annotation system is proposed for evaluating multi-target tracking using 2D laser scanners. The proposed annotation system uses the existing MTT algorithm to generate initial annotation results and uses camera images as the strong hints to assist annotators to recognize moving objects in laser scans. The annotators can draw the object's shape and future trajectory to automate segmentation and data association and reduce the annotation task loading. The user study results show that the performance of the proposed annotation system is superior in the V-measure vs. annotation speed tests and the false positive and false negative rates.

关键词： Trajectory Shape Accuracy Lasers Cameras Image segmentation Benchmark testing

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