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Conference on Multisensor, Multisource Information Fusion

作者： Wang, TY Han, YSS Varshney, PK Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819453579

In this paper, we consider the distributed classification problem in wireless sensor networks. The DCFECC-SD approach employing the binary code matrix has recently been proposed to cope with the errors caused by both sensor faults and the effect of fading channels. The DCFECC-SD approach extends the DCFECC approach by using soft decision decoding, to combat channel fading. However, the performance of the system employing the binary code matrix could be degraded if the distance between different hypotheses can not be kept large. This situation could happen when the number of sensor is small or the number of hypotheses is large. In this paper, we design the DCFECC-SD approach employing the D-ary code matrix, where D > 2. Simulation results show that the performance of the DCFECC-SD approach employing the D-ary code matrix is better than that of the DCFECC-SD approach employing the binary code matrix. Performance evaluation of DCFECC-SD using different number of bits of local decision information is also provided when the total channel energy output from each sensor node is fixed.

关键词： distributed classification wireless sensor networks coding soft-decision decoding decision fusion multisensor fusion fading channel

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IN-NETWORK ADAPTIVE CLUSTER ENUMERATION FOR distributed classification AND LABELING 24

IN-NETWORK ADAPTIVE CLUSTER ENUMERATION FOR DISTRIBUTED CLAS...

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24th European Signal Processing Conference (EUSIPCO)

作者： Teklehaymanot, Freweyni K. Muma, Michael Liu, Jun Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp Merckstr 25 D-64283 Darmstadt Germany Tech Univ Darmstadt Grad Sch CE Dolivostr 15 D-64293 Darmstadt Germany

ISBN: (纸本)9780992862657

A crucial first step for signal processing decentralized sensor networks with node-specific interests is to agree upon a common unique labeling of all observed sources in the network. The knowledge "who observes what" is required, e.g. in node-specific audio or video signal enhancement to form node clusters of common interest. Recently proposed in-network distributed adaptive classification and labeling algorithms assume knowledge on the number of objects (clusters), which is not necessarily available in real-world applications. Thus, we consider the problem of estimating the number of data-clusters in the distributed adaptive network set-up. We propose two distributed adaptive cluster enumeration methods. They combine the diffusion principle, where the nodes share information within their local neighborhood only (without fusion center), with the X-means and the PG-means cluster enumeration. Performance is evaluated via simulations and the applicability of the methods is illustrated using a distributed camera network where moving objects appear and disappear from the Line-of-Sight (LOS) and the number of clusters becomes time-varying.

关键词： distributed Cluster Enumeration distributed classification Object Labeling Camera Network X-means PG-means MDMT Diffusion

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Collaborative Signal Processing for Action Recognition in Body Sensor Networks: A distributed classification Algorithm Using Motion Transcripts

Collaborative Signal Processing for Action Recognition in Bo...

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9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

作者： Ghasemzadeh, Hassan Loseu, Vitali Jafari, Roozbeh Univ Texas Dallas Dept Elect Engn Embedded Syst & Signal Proc Lab Richardson TX 75080 USA

ISBN: (纸本)9781605589886

Body sensor networks are emerging as a promising platform for remote human monitoring. With the aim of extracting bio-kinematic parameters from distributed body-worn sensors, these systems require collaboration of sensor nodes to obtain relevant information from an overwhelmingly large volume of data. Clearly, efficient data reduction techniques and distributed signal processing algorithms are needed. In this paper, we present a data processing technique that constructs motion transcripts from inertial sensors and identifies human movements by taking collaboration between the nodes into consideration. Transcripts of basic motions, called primitives, are built to reduce the complexity of the sensor data. This model leads to a distributed algorithm for segmentation and action recognition. We demonstrate the effectiveness of our framework using data collected from five normal subjects performing ten transitional movements. The results clearly illustrate the effectiveness of our framework. In particular, we obtain a classification accuracy of 84.13% with only one sensor node involved in the classification process.

关键词： Body Sensor Networks Collaborative Signal Processing distributed classification Motion Transcripts

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Maximum Entropy Modeling for distributed classification, Regression, and Interaction Discovery

Maximum Entropy Modeling for Distributed Classification, Reg...

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作者： Zhang, Yanxin PennState University Libraries

学位级别：Doctor of Philosophy

The maximum entropy (ME) principle has been widely applied to specialized applications in statistical learning and pattern recognition. The concept of ME method is to find a probability distribution that satisfies whatever information is available from known data in the form of constraints. The ME solution is the unique Gibbs distribution that maximizes the likelihood of the training data. In this dissertation, we develop ME methods with applications to three important tasks, i.e., distributed classification, regression, and identification of feature interactions. In the distributed classification paradigms, where common labeled data may be not available for designing classifier ensemble, traditional fixed decision aggregation such as voting, averaging, or naive Bayes rules could not account for class prior mismatch or classifier dependencies. Previous transductive learning strategies have several drawbacks, e.g., feasibility of the constraints was not guaranteed and heuristic learning was applied. We overcome these problems by proposing a transductive maximum entropy (TME) model for designing aggregation to satisfy the constraints in local classifiers. We augment the test set support to ensure the feasibility of the constraints and develop transductive iterative scaling (TIS) algorithm for optimal solution. This method is shown to achieve improved decision accuracy over the earlier transductive approaches and fixed rules on a number of UC Irvine data ***, ME models have been developed for classification on discrete feature spaces, i.e., both the output variable and input features are categorical or ordinal. We extend ME model for the regression problem, where the output variable and input features are mixed continuous-discrete valued. We propose a hierarchical maximum entropy (HME) model for regression in building a posterior model for the output variable, which encodes constraints involving hierarchical derived features that are obtained by agglomerativ

关键词： interaction discovery maximum entropy distributed classification regression

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Data-based distributed classification and Its Performance Analysis

Data-based Distributed Classification and Its Performance An...

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International Conference on Information Fusion

作者： Sandeep Gutta Qi Cheng School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 USA

ISBN: (纸本)9780982443859

distributed classification using multimodal sensors is a problem of very high practical importance. Most of the existing distributed classification systems are designed under the assumptions that prior class probabilities, and/or observation models are known. In this paper, we design a distributed classification system without requiring any prior model information. Specifically, at each local sensor, multiple binary support vector machine (SVM) based classifiers are used and each classifier is trained to distinguish one class from the rest. At the fusion center, the Dempster-Shafer theory is adopted to effectively combine the evidence from all SVMs with appropriately defined basic probability assignments. The final decision is made by selecting the class with the highest belief. Theoretical performance prediction methods are proposed for the designed classification system. Through experiments on a synthetic dataset and the benchmark 1999 KDD intrusion detection dataset, we demonstrate the effectiveness of the evaluation method and the superiority of the proposed framework over the conventional Bayesian cost based fusion rule in this context.

关键词： distributed classification binary support vector machines Dempster-Shafer theory basic probability assignments

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Collaborative Classifier Agents: Studying the Impact of Learning in distributed Document classification 07

Collaborative Classifier Agents: Studying the Impact of Lear...

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7th ACM/IEEE Joint Conference on Digital Libraries

作者： Ke, Weimao Mostafa, Javed Fu, Yueyu Indiana Univ Lab Appl Informat Res Bloomington IN 47405 USA

ISBN: (纸本)9781595936448

We developed a multi-agent framework where agents had limited/distributed knowledge for document classification and collaborated with each other to overcome the knowledge distribution. Each agent was equipped with a certain learning algorithm for predicting potential collaborators, or helping agents. We conducted experimental research on a standard news corpus to examine the impact of two learning algorithms: Pursuit Learning and Nearest Centroid Learning. For a fundamental retrieval operation, namely classification, both algorithms achieved competitive classification effectiveness and efficiency. Subsequently, the impact of the learning exploration rate and the maximum collaboration range on classification effectiveness and efficiency were examined. Close investigation of agent learning dynamics revealed increasing and stabilizing patterns that were enhanced by the learning algorithms.

关键词： Information retrieval distributed classification multi-agent system learning collaboration effectiveness efficiency

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distributed Data classification in Sensor Networks

Distributed Data Classification in Sensor Networks

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29th ACM SIGACT-SIGOPS Symposium on Principles of distributed Computing

作者： Eyal, Ittay Keidar, Idit Rom, Raphael Technion Israel Inst Technol IL-32000 Haifa Israel

ISBN: (纸本)9781605588889

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we define the distributed classification problem, in which numerous interconnected nodes compute a classification of their data, i.e., partition these values into multiple collections, and describe each collection concisely. We present a generic algorithm that solves the distributed classification problem and may be implemented in various topologies, using different classification types. For example, the generic algorithm can be instantiated to classify values according to distance, like the famous k-means classification algorithm. However, the distance criterion is often not sufficient to provide good classification results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for classification decisions. Simulations show the robustness and speed of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, classification criterion and collection representation, in fully asynchronous settings.

关键词： distributed classification Gossip

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distributed Automatic Modulation classification With Multiple Sensors

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IEEE SENSORS JOURNAL 2010年第11期10卷 1779-1785页

作者： Xu, Jefferson L. Su, Wei Zhou, MengChu New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Tongji Univ Dept Comp Sci & Engn Shanghai 201804 Peoples R China USA RDECOM Commun Elect RD&E Ctr Ft Monmouth NJ 07703 USA

Automatic modulation classification (AMC) has been intensively studied to enhance the successful classification rate, particularly for overcoming the physical limit that deals with weak signals received in a noncooperative communication environment. A wireless sensor network (WSN) has multiple geometrically distributed sensors to work cooperatively. The distributed signal sensing and classification performed by collaborated sensors is proven to be beneficial to increasing the modulation classification reliability. In this paper, we apply the likelihood ratio-based distributed detection fusion technique to address the issues of general binary modulation classifications. The data fusion algorithm performed in the primary node is presented. Its numerical performance with simulation results is demonstrated.

关键词： Cognitive radio distributed classification distributed detection likelihood ratio test (LRT) modulation classification wireless sensor networks (WSN)

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A combined decision fusion and channel coding scheme for distributed fault-tolerant classification in wireless sensor networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2006年第7期5卷 1695-1705页

作者： Wang, Tsang-Yi Han, Yunghsiang S. Chen, Biao Varshney, Pramod K. Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Sanhsia Taiwan Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to a fusion center through fading channels. classification performance could be degraded due to the errors caused by both sensor faults and fading channels. Integrating channel decoding into the distributed fault-tolerant classification fusion algorithm, we obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any redundancy. The soft decoding scheme is utilized to combat channel fading, while the distributed classification fusion structure using error correcting codes provides good sensor fault-tolerance capability. Asymptotic performance of the proposed approach is also investigated. Performance evaluation of the proposed approach with both sensor faults and fading channel impairments is carried out. These results show that the proposed approach outperforms the system employing the MAP fusion rule designed without regard to sensor faults and the multiclass equal gain combining fusion rule.

关键词： distributed classification coding Soft-Decision Decoding Decision fusion Multisensor fusion wireless sensor net-works Fading channels.

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Optimized distributed Automatic Modulation classification in Wireless Sensor Networks Using Information Theoretic Measures

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IEEE SENSORS JOURNAL 2017年第10期17卷 3079-3091页

作者： Hakimi, Saeed Hodtani, Ghosheh Abed Ferdowsi Univ Mashhad Dept Elect Engn Mashhad *** Iran

Automatic modulation classification of digital signals is essential for intelligent communication systems. This paper addresses the distributed classification of digital amplitudephase modulated signals in a system of multiple sensors, which observe the unknown signal corrupted with the additive white Gaussian noise. The sensors are connected to a fusion center through block-fading orthogonal multiple access channels. We introduce a new method, where: 1) for classification, an information-theoretic similarity measure known as correntropy is exploited by each local sensor;2) for transmitting local decisions, an estimation of a priori probabilities is used by local sensors;and 3) for optimizing power allocation to each sensor, the Bhattacharyya distance is employed as the objective function by the fusion center. The proposed scheme improves the classification accuracy through exploiting channel diversity, which, in turn, enhances the overall performance. Simulation results validate the theoretically claimed improvement in the performance.

关键词： Automatic modulation classification (AMC) Bhattacharyya distance correntropy distributed classification wireless sensor networks (WSNs)

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