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

作者： Qiang Ma Kebin Liu Xin Miao Yunhao Liu Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China MOE Key Laboratory for Information System Security School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781467307734

Traditional approaches for wireless sensor network diagnosis are mainly sink-based. They actively collect global evidences from sensor nodes to the sink so as to conduct centralized analysis at the powerful back-end. On the one hand, long distance proactive information retrieval incurs huge transmission overhead; On the other hand, due to the coupling effect between diagnosis component and the application itself, sink often fails to obtain complete and precise evidences from the network, especially for the problematic or critical parts. To avoid large overhead in evidence collection process, self-diagnosis injects fault inference modules into sensor nodes and let them make local decisions. Diagnosis results from single nodes, however, are generally inaccurate due to the narrow scope of system performances. Besides, existing self-diagnosis methods usually lead to inconsistent results from different inference processes. How to balance the workload among the sensor nodes in a diagnosis task is a critical issue. In this work, we present a new in-network diagnosis approach named Local-Diagnosis (LD2), which conducts the diagnosis process in a local area. LD2 achieves diagnosis decision through distributed evidence fusion operations. Each sensor node provides its own judgements and the evidences are fused within a local area based on the Dempster-Shafer theory, resulting in the consensus diagnosis report. We implement LD2 on TinyOS 2.1 and examine the performance on a 50 nodes indoor testbed.

关键词： Wireless sensor networks Bayesian methods Measurement Accuracy Reliability Computer crashes Protocols

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WILL: Wireless indoor localization without site survey

WILL: Wireless indoor localization without site survey

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

作者： Chenshu Wu Zheng Yang Yunhao Liu Wei Xi MOE Key Laboratory for Information System Security School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University China Department of Computer Science and Engineering HKUST Hong Kong China Xian Jiaotong University China

ISBN: (纸本)9781467307734

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600m 2 , and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

关键词： Fingerprint recognition Floors Databases Skeleton Mobile handsets IEEE 802.11 Standards

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An online semantic tree for link detection

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Journal of Convergence information Technology 2012年第5期7卷 147-157页

作者： He, Ruifang Hong, Yu Information System and Software Engineering Lab School of Computer Science and Technology Tianjin University Tianjin 15001 China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou 215006 China

Link Detection (abbr. LDT) is to determine whether two stories discuss the same topic in Topic Detection and Tracking (abbr. TDT) track. The key issue is to correctly measure the relevance between two stories. Most researches on LDT use a series of independent words to describe stories (each story is a text specially discussing news), and the relevance between two stories is determined based on the percentage and weight of overlapping words between them. Although substantial improvement has been achieved, inadequate descriptions of word sense and semantics still have negative influences on the accuracy of LDT. In this paper we propose an online semantic tree, which is hierarchically constructed by the most relevant words extracted from previous story streams. In online semantic tree, word sense is described by a series of words in a sense closed-loop, and semantic relation among words is measured by depth and width of level that words locate in. In LDT, online semantic tree is built for each story, and the relevance between two stories is determined by measuring the KL divergence between their online semantic trees. The method performs quite well on TDT4 corpus. The Min Norm CDet of the method in testing is 0.2274 lower than that of the baseline.

关键词： Semantics

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Towards energy-fairness in asynchronous duty-cycling sensor networks

Towards energy-fairness in asynchronous duty-cycling sensor ...

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

作者： Zhenjiang Li Mo Li Yunhao Liu Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China School of Computer Engineering Nanyang Technological University Singapore MOE Key Laboratory for Information System Security School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781467307734

In this paper, we investigate the problem of controlling node sleep intervals so as to achieve the min-max energy fairness in asynchronous duty-cycling sensor networks. We propose a mathematical model to describe the energy efficiency of such networks and observe that traditional sleep interval setting strategy, i.e., operating sensor nodes with identical sleep intervals, or intuitive control heuristics, i.e., greedily increasing sleep intervals of sensor nodes with high energy consumption rates, hardly perform well in practice. There is an urgent need to develop an efficient sleep interval control strategy for achieving fair and high energy efficiency. To this end, we theoretically formulate the Sleep Interval Control (SIC) problem and find it a convex optimization problem. By utilizing the convex property, we decompose the original problem and propose a distributed algorithm, called GDSIC. In GDSIC, sensor nodes can tune sleep intervals through a local information exchange such that the maximum energy consumption rate in the network approaches to be minimized. The algorithm is self-adjustable to the traffic load variance and is able to serve as a unified framework for a variety of asynchronous duty-cycling MAC protocols. We implement our approach in a prototype system and test its feasibility and applicability on a 50-node testbed. We further conduct extensive trace-driven simulations to examine the efficiency and scalability of our algorithm with various settings.

关键词： Energy consumption Silicon carbide Protocols Receivers Wireless sensor networks Data communication Switching circuits

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Human action recognition based on sparse representation induced by L1/L2 regulations

Human action recognition based on sparse representation indu...

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International Conference on Pattern Recognition

作者： Zan Gao An-an Liu Hua Zhang Guang-ping Xu Yan-bing Xue Key Laboratory of Computer Vision and System Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Department of Electronic Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781467322164

Sparse representation based classification (SRC) has been widely used for face recognition (FR). Although SRC algorithm is also adopted in human action recognition, the evaluations of different regular terms have not been given. In this paper, we will discuss and evaluate the role of different regular terms of SRC in human action recognition, after that, we propose human action recognition algorithm based on sparse representation induced by L 1 and L 2 regulations - called SR-L12. Experiments on well known KTH action dataset show that SR-L12 is much better than that of nearest neighbor (NN), nearest subspace (NS), full-space (NF), SRC and collaborative representation classification (CRC). Moreover, the proposed method is comparable to most of state-of-the-art algorithms for human action recognition.

关键词： Humans Accuracy Noise measurement Testing Training Classification algorithms Feature extraction

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Priv-Code: Preserving privacy against traffic analysis through network coding for multihop wireless networks

Priv-Code: Preserving privacy against traffic analysis throu...

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

作者： Zhiguo Wan Kai Xing Yunhao Liu MOE Key Laboratory for Information System Security School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University China School of Computer Science and Technology University of Science and Technology China Department of Computer Science and Engineering HKUST Hong Kong China

ISBN: (纸本)9781467307734

Traffic analysis presents a serious threat to wireless network privacy due to the open nature of wireless medium. Traditional solutions are mainly based on the mix mechanism proposed by David Chaum, but the main drawback is its low network performance due to mixing and cryptographic operations. We propose a novel privacy preserving scheme based on network coding called Priv-Code to counter against traffic analysis attacks for wireless communications. Priv-Code is able to provide strong privacy protection for wireless networks as the mix system because of its intrinsic mixing feature, and moreover, it can achieve better network performance owing to the advantage of network coding. We first construct a hypergraph-based network coding model for wireless networks, under which we formalize an optimization problem whose objective function is to make each node have identical transmission rate. Then we provide a decentralized algorithm for this optimization problem. After that we develop an information theoretic metric for privacy measurement using entropy, and based on this metric we demonstrate that Priv-Code achieves stronger privacy protection than the mix system while achieving better network performance.

关键词： Optimization

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Study on key technology of database integration system based on SOAP

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International Journal of Advancements in Computing Technology 2012年第18期4卷 268-274页

作者： Wang, Weihua Wang, Weiqing School of Software Engineering Chongqing University of Arts and Sciences Yongchuan Chongqing China Department of Information Management Southwest University Rongchang Campus Rongchang Chonqing China Key Laboratory of Machine Vision and Intelligent Information System Chongqing University of Arts and Sciences Yongchuan Chongqing China

The distributed and heterogeneous database system of a enterprise can be resolved by the development of the technology of Web Service, and the transforming of the data type in the heterogeneous database is the key problem of this method. To resolve this problem, a technique based on SOAP protocol is proposed. As we all know that the SOAP is a public and standard protocol, and all of the heterogeneous database information can be transformed into xml format information by the SOAP protocol which can be known to all of the database system user, so, the information can be integrated by the SOAP protocol effectually, and if the SOAP technology combine with the Web Service technology, the system has the benign expansibility.

关键词： Web services

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Optimal density estimation for exposure-path prevention in wireless sensor networks using percolation theory

Optimal density estimation for exposure-path prevention in w...

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

作者： Liang Liu Xi Zhang Huadong Ma Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University-áof Posts and-áTelecommunications Beijing China Networking and Information System Laboratory Department of Electrical and Computer Engineering Texas A and M University College Station TX USA

ISBN: (纸本)9781467307734;9781467307758

Most existing works on sensor coverage mainly concentrate on the full coverage models which ensure that all points in the deployment region are covered at the expense of high complexity and cost. In contrast, the exposure-path prevention does not require full coverage sensor deployment, and instead it only needs the partial coverage, because the exposure paths are prevented as long as no moving objects or phenomena can go through a deployment region without being detected. Towards this end, we focus on the partial coverage by applying the percolation theory to solve the exposure path problem for wireless sensor networks. Specifically, we propose a bond-percolation based scheme by mapping the exposure path problem into a bond percolation model. Using this model, we derive the analytical expressions of critical densities for wireless sensor networks under random sensor deployment.

关键词： Lattices Wireless sensor networks Simulation Upper bound Educational institutions Approximation methods Wireless networks

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Mechanism design for finding experts using locally constructed social referral web

Mechanism design for finding experts using locally construct...

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

作者： Lan Zhang Xiang-Yang Li Yunhao Liu QiuYuan Huang Shaojie Tang MOE Key Laboratory for Information System Security School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University China Department of Computer Science Illinois Institute of Technology USA Department of Computer Science and Engineering HKUST Hong Kong China Department of Electrical Engineering University of Florida USA

ISBN: (纸本)9781467307734;9781467307758

In this work, we address the problem of finding experts using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the search performance, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several datasets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90% in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20%. The overall cost of finding an expert by our truthful mechanism is about 20% of the untruthful method and only about 2% of the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

关键词： Vectors Probability Search problems Facebook Relays Databases

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Dual transfer learning

Dual transfer learning

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12th SIAM International Conference on Data Mining, SDM 2012

作者： Long, Mingsheng Wang, Jianmin Ding, Guiguang Cheng, Wei Zhang, Xiang Wang, Wei Department of Computer Science and Technology Tsinghua University Beijing 100084 China School of Software Tsinghua University Beijing 100084 China Key Laboratory for Information System Security Ministry of Education United States Department of Computer Science University of North Carolina Chapel Hill NC United States Dept of Electrical Engineering and Computer Science Case Western Reserve University OH United States

ISBN: (纸本)9781611972320

Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the tar- get domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applica- Tions. The general idea of the existing methods is to utilize the common latent structure shared across domains as the bridge for knowledge transfer. These methods usually model the common latent structure by using either the marginal distribution or the conditional distribution. However, with- out exploring the duality between these two distributions, these single bridge methods may not achieve optimal capa- bility of knowledge transfer. In this paper, we propose a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the du- Ality between them in a principled way. The key idea behind DTL is that learning one distribution can help to learn the other. This duality property leads to mutual reinforcement when adapting both distributions across domains to trans- fer knowledge. The proposed method is formulated as an optimization problem based on joint nonnegative matrix tri- factorizations (NMTF). The two distributions are learned from the decomposed latent factors that exhibit the duality property. An efficient alternating minimization algorithm is developed to solve the optimization problem with con- vergence guarantee. Extensive experimental results demon- strate that DTL is more effective than alternative transfer learning methods. Copyright © 2012 by the Society for Industrial and Applied Mathematics.

关键词： Optimization

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