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A Dynamic Host Selection Algorithm for Layered Data Storage Architecture in a Pervasive Space

A Dynamic Host Selection Algorithm for Layered Data Storage ...

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International Conference on Complex, Intelligent and software Intensive Systems, CISIS

作者： Zhuzhong Qian Ilsun You Youyou Lu Sanglu Lu State Key Laboratory of Novel Software Technology Nanjing University Nanjing China School of Information Science Korean Bible University South Korea Department of Computer Science and Technology Tsinghua University Beijing China

Context data is important information for the behaviors of the applications in a pervasive space. To effectively restore huge amount of data, tree-liked layered storage architecture are proposed, where the leaf nodes collect the data from the sensing devices located in its domain. However, the sensing devices may be moving among different domains. In order to integrate the data from the same device, related leaf nodes should upload and store the data to a certain up-layer node, called host node. This paper presents a deep study of the data storage problem and proposes an online algorithm DHS to dynamically select the host node, which reduces the communication cost significantly. We prove the correctness of the algorithm theoretically. The experiment results also show that DHS is correct and effective.

关键词： Heuristic algorithms Memory Context Space technology Mobile communication computer architecture Costs RFID tags Pervasive computing Wireless sensor networks

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CASTLE: A computer-assisted stress teaching and learning environment for learners of English as a Second Language

CASTLE: A computer-assisted stress teaching and learning env...

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作者： Lu, Jingli Wang, Ruili De Silva, Liyanage C. Gao, Yang Liu, Jia SEAT Massey University Palmerston North New Zealand State Key Laboratory for Novel Software Technology Nanjing University China Faculty of Science University of Brunei Darussalam Brunei Department of Electronic Engineering Tsinghua University China

In this paper, we describe the principle and functionality of the computer-Assisted Stress Teaching and Learning Environment (CASTLE) that we have proposed and developed to help learners of English as a Second Language (ESL) to learn stress patterns of English language. There are three modules in the CASTLE system. The first module, individualised speech learning material providing module, can provide learners individualised speech material that possesses their preferred voice features, e.g., gender, pitch and speech rate. The second module, perception assistance module, is intended to help learners correctly perceive English stress patterns, which can automatically exaggerate the differences between stressed and unstressed syllables in a teacher's voice. The third module, production assistance module, is developed to help learners to be aware of the rhythm of English language and provide them feedback in order to improve their production of stress patterns. © 2010 ISCA.

关键词： computer aided instruction

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Compositional abstraction refinement for timed systems

Compositional abstraction refinement for timed systems

引用

2010 4th International Symposium on Theoretical Aspects of software Engineering, TASE 2010

作者： He, Fei Zhu, He Hung, William N. N. Song, Xiaoyu Gu, Ming School of Software Tsinghua University Beijing China Tsinghua National Laboratory for Information Science and Technology China Key Laboratory for Information System Security MOE China Synopsys Inc. Mountain View CA United States Department of ECE Portland State University Portland OR United States

ISBN: (纸本)9780769541488

Model checking suffers from the state explosion problem. Compositional abstraction and abstraction refinement have been investigated in many areas to address this problem. This paper considers the compositional model checking for timed systems. We present an automated approach which combines compositional abstraction and counter-example guided abstraction refinement (CEGAR). The proposed approach exploits the semantics of a timed automaton to procure its over-approximative abstraction. Any safety property which holds on the abstraction is guaranteed to hold on the concrete model. In the case of a spurious counter-example, our proposed approach refines and strengthens the abstraction in a component-wise method. We implemented our method with the model checking tool Uppaal. Experimental results show promising improvements. © 2010 IEEE .

关键词： Model checking

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Ant routing optimization algorithm for extending the lifetime of wireless sensor networks

Ant routing optimization algorithm for extending the lifetim...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiao-Min Hu Jun Zhang Department of Computer Science Key Laboratory of Digital Life Ministry of Education Key Laboratory of Software Technology Education Department of Guangdong Province Sun Yat-Sen University China

Since sensors are energy-limited in a wireless sensor network (WSN), how to balance the energy consumption among the sensors in the network so as to prolong the lifetime of the whole network is a critical research topic in the development of WSNs. In this paper, a novel ant routing optimization (ARO) algorithm is proposed for taking advantage of the redundant sensors in the network to help relay the sensed data traffic to the sink. By choosing suitable relay nodes, the lifetime of the sensing sensors are extended, resulting in the extension of the whole network lifetime. The ARO algorithm searches energy-efficient routing trees by considering the energy consumption of sensing, data transmission and reception. Simulation results show that ARO finds much better routing trees than the minimum transmission energy routing scheme.

关键词： Wireless sensor networks

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An associative memory system for incremental learning and temporal sequence

An associative memory system for incremental learning and te...

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International Joint Conference on Neural Networks (IJCNN)

作者： Furao Shen Hui Yu Wataru Kasai Osamu Hasegawa State Key Laboratory for Novel Software Technology Jiangyin Information Technology Research Institute Nanjing University Nanjing China Department of Computer Intelligence and Systems Science Tokyo Institute of Technology Yokohama Japan Imaging Science and Engineering Laboratory Tokyo Institute of Technology Yokohama Japan

An associative memory (AM) system is proposed to realize incremental learning and temporal sequence learning. The proposed system is constructed with three layer networks: The input layer inputs key vectors, response vectors, and the associative relation between vectors. The memory layer stores input vectors incrementally to corresponding classes. The associative layer builds associative relations between classes. The proposed method can incrementally learn key vectors and response vectors; store and recall both static information and temporal sequence information; and recall information from incomplete or noise-polluted inputs. Experiments using binary data, real-value data, and temporal sequences show that the proposed method works well.

关键词： Associative memory Training Context Indexes Prototypes Humans Topology

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Analyzing Real-time Schedulability of Resource-oriented Distributed Real-time Embedded software

Analyzing Real-time Schedulability of Resource-oriented Dist...

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2010 2nd IEEE International Conference on Information Management and Engineering(2010年IEEE第二届信息管理与工程国际会议 IEEE ICIME 2010)

作者： Liqiong Chen Yunxiang Liu Guisheng Fan Department of Computer Science and Information EngineeringShanghai Institute of Technology Shanghai Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai 201112 China Departme

As computer systems become increasingly internetworked, Distributed Realtime Embedded (DRE) software is becoming increasingly common and important, a challenging problem faced by researchers and developers of DRE software is how to devise and implement a method that can effectively analyze requirements in varying operational conditions. In this paper, a Distributed Realtime Embedded net (DREN) is proposed as software design specification modeling tool. The basic tasks, function module and communication process are modeled by using DREN, thus forming the entire application by making use of Petri net’s synthesis operation. Time Reachability Graph is adopted to analyze the correctness of DRENmodel, the basic properties of DRE system are also considered. Finally, a specific example is given to simulate the analytical process, the results show that the method can be a good solution to analyze DRE software.

关键词： Embedded software Real time systems Distributed computing Embedded computing Information analysis computer science Application software Processor scheduling Laboratories software testing

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Learning spectral graph mapping for classification

Learning spectral graph mapping for classification

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Xiao-Hua Xu Ping He Ling Chen Department of Computer Science Yangzhou University Yangzhou China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Novel Software Technology Nanjing University Nanjing China

Nonlinear multi-classification has been a popular task in machine learning recently. In this paper, we propose a nonlinear multi-classification algorithm named Supervised Spectral Space Classifier (S 3 C), S 3 C integrates the discriminative information into the spectral graph mapping and transforms the input data into the low-dimensional supervised spectral space. S 3 C not only enables researchers to examine the mapped data in its supervised spectral space, but also can be directly applied to multi-classification problems. Experimental results on synthetic and real-world datasets demonstrate that S 3 C outperforms the state-of-the-art nonlinear classifiers SVM.

关键词： Blood Spirals Glass Breast Ionosphere Yttrium

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Improving Prediction Accuracy of Matrix Factorization Based Network Coordinate Systems

Improving Prediction Accuracy of Matrix Factorization Based ...

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International Conference on computer Communications and Networks (ICCCN)

作者： Yang Chen Peng Sun Xiaoming Fu Tianyin Xu Institute of Computer Science University of Goettingen Germany Department of Electronic Engineering Tsinghua University China State Key Laboratory of Novel Software and Technology Nanjing University China

Network Coordinate (NC) systems provide a lightweight and useful way for scalable Internet distance prediction while serving as an important component in many Peer-to-Peer applications. Most of the existing NC systems utilize the Euclidean distance model, which is largely impaired by the persistent occurrence of Triangle Inequality Violation (TIV) in the Internet. Recently, matrix factorization (MF) based NC systems, which can completely remove the TIV constraint, provide an alternative approach towards better prediction accuracy. Phoenix, an NC system based on the MF model, well explores the advantage of the MF model and becomes the most accurate NC system so far. However, through experimental study, we find that the prediction accuracy of Phoenix for short links is significantly worse compared with the overall prediction accuracy. Based on the observation, we propose a new NC system, named Pancake, aiming at reducing the high prediction error for short links. By introducing a two-level architecture, Pancake achieves much higher prediction accuracy than other selected existing NC systems. Through extensive experiments, we demonstrate that Pancake reduces the 90th percentile relative error by up to 25.37% from Phoenix. Moreover, Pancake converges very fast and is robust to different dimension values. For further insights, we study the performance of Pancake using a dynamic data set in addition to the widely used aggregate data sets. With varying RTTs over time, Pancake outperforms other NC systems consistently.

关键词： Accuracy Internet Clustering algorithms Euclidean distance Prediction algorithms Extraterrestrial measurements Algorithm design and analysis

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D-TDMA: An Approach of Dynamic TDMA Scheduling for Target Tracking in Wireless Sensor Networks

D-TDMA: An Approach of Dynamic TDMA Scheduling for Target Tr...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yun Wang Peizhong Shi Kai Li Zhikui Chen Department of Computer Science & Engineering Key Laboratory of Computer Network and Information Integration MOE South-East University Nanjing China School of software technology Dalian university of technology Dalian China

ISBN: (纸本)9781424497799

In the application of target tracking based on dynamic convey tree-based collaboration (DCTC) in wireless sensor networks, collisions and interferences among nodes pose a challenge for data aggregation. In this paper, we refine slot allocation to the nodes in dynamic convey tree and design an energy efficient MAC protocol called D-TDMA, which avoids collisions and interferences and allocates contiguous active slots to nodes as far as possible during data aggregation from leaf nodes to a root node. As a result, the energy consumption in switching from sleeping to active state is saved. Simulation results show that D-TDMA not only gets rid of the impact of collisions and interferences among nodes in dynamic convey tree, but also performs similar throughput to that of CSMA-like mechanism where nodes work without active/sleep scheduling strategy. Our protocol outperforms ESR in both energy efficiency and low delay.

关键词： Wireless sensor networks Target tracking Dynamic scheduling Interference Resource management Protocols Sensors

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Approximate Data Collection for Wireless Sensor Networks

Approximate Data Collection for Wireless Sensor Networks

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chao Wang Huadong Ma Yuan He Shuguang Xiong Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Department of Computer Science and Technology Harbin Institute of Technology Harbin China

Data collection is a fundamental issue in wireless sensor networks. In many application scenarios for sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with given error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform a global approximate data collection on the sink according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of several subsets of sensor nodes, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and use a greedy heuristic algorithm to find an approximate solution. We also propose a monitoring algorithm to adjust these subsets according to the changes of sensor readings. Our trace-driving simulation results show that our data collection approach ADC can notably reduce the communication cost with given error bounds.

关键词： Approximation methods Estimation Data models Correlation Approximation algorithms Clustering algorithms Wireless sensor networks

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