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Mobile-Agent-Based distributed Decision Tree classification in Wireless Sensor Networks

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2008年第6期24卷 1787-1798页

作者： Wang, Shuang-Quan Chen, Xin Chen, Ning-Jiang Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Philips Res E Asia Shanghai 200070 Peoples R China

Wireless sensor networks (WSNs) connect physical sensors that are distributed in the environment. In many applications, the statistical pattern recognition methods, such as decision tree (DT) algorithms, are used to recognize the patterns of the sensor readings. To enable the decision tree classification (DTC) in WSNs, a new distributed decision tree classification (DDTC) algorithm based on mobile agent is proposed in this paper. We organize the conjunctive sets of linear classifiers in DT into groups of operations on attributes. Each group of operations is allocated to a single sensor node. If a mobile agent visits these sensor nodes serially, the recognition result can be acquired step by step. Thus the sensor nodes do not need to transmit all the sensor data to a centralized node where all the data processing is carried out in traditional DTC. In DDTC, if not all the attributes are needed for operation, classification of one instance can finish halfway. Two public data sets are used to evaluate the performance of DTC and DDTC on energy consumption. The simulation results indicate that, compared with the centralized DTC, the DDTC algorithm decreases the number of transmissions, balances the power consumption and computation among sensor nodes, and prolongs the lifetime of the network.

关键词： mobile agent decision tree distributed classification wireless sensor networks energy consumption

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distributed fault-tolerant classification in wireless sensor networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2005年第4期23卷 724-734页

作者： Wang, TY Han, YSS Varshney, PK Chen, PN Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13210 USA Natl Chi Nan Univ Dept Comp Sci & Informat Engn Nantou 545 Taiwan Natl Chiao Tung Univ Dept Commun Engn Hsinchu 30050 Taiwan

Fault-tolerance and data fusion have been considered as two fundamental functions in wireless sensor networks. In this paper, we propose a novel approach for distributed multiclass classification using a fault-tolerant fusion rule for wireless sensor networks. Binary decisions from local sensors, possibly in the presence of faults, are forwarded to the fusion center that determines the final classification result. classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces computation time and memory requirements at the fusion center. Code matrix design is essential for the design of such systems. Two efficient code matrix design algorithms are proposed in this paper. The relative merits of both algorithms are also studied. We also develop sufficient conditions for asymptotic detection of the correct hypothesis by the proposed approach. Performance evaluation of the proposed approach in the presence of faults is provided. These results show significant improvement in fault-tolerance capability as compared with conventional parallel fusion networks.

关键词： data fusion decision fusion distributed classification error correcting codes fault-tolerance multisensor systems wireless sensor networks (WSNs)

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distributed fault-tolerant classification fusion using error correcting codes

Distributed fault-tolerant classification fusion using error...

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Conference on Multisensor, Multisource Information Fusion - Architectures, Algorithms and Applications 2003

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

ISBN: (纸本)0819449598

In this paper we propose a new approach for distributed multiclass classification using a hierarchical fusion architecture. Binary decisions from local sensors, possibly in the presence of faults, axe fused locally. Locally fused results are forwarded to the global fusion center that determines the final classification result. classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces bandwidth requirements as well as computation time and memory requirements at the fusion center. Numerical examples axe provided to illustrate the performance of this new approach.

关键词： distributed classification decision fusion multisensor fusion error correcting codes fault-tolerance

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classification of horizontally distributed data using cooperative learners and correlation-based region-specific partnership score

Classification of horizontally distributed data using cooper...

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International Multiconference of Engineers and Computer Scientists

作者： Deshmeh, Gholamhossein Barforoush, Ahmad Abdollahzadeh AmirKabir Univ Technol Tehran Iran

ISBN: (纸本)9789889867140

We address the problem of classification of horizontally distributed data, where only limited sharing is allowed for each learner, in terms of a share ratio limit parameter, and no single learner is allowed to access the whole training dataset (neither at once nor incrementally). We propose a classifier ensemble creation method, where a global context set, formed via combining context sets shared by local learners, is used by each local learner to choose a partner for each region of the its own dataset. Learners receive data instances and predictions on from their partners and use those to retrain themselves. A voting scheme is constructed for every test instance, by an organizer agent, using the generalization capability of each participating learner in the data subspace around the corresponding test instance. The contributions of our work are: 1) calculating a partnership score (PS) for partner selection, using a) region-specific classification capability of potential partners and b) region-specific correlation analysis, 2) constructing voting schemes based on individual test instances, while considering the generalization capability of each learner in the data subspace around the test instance, and 3) privacy preserving by avoiding the gathering of all data in one site, or access to all data by one learner, while achieving accuracy comparable to, and sometimes higher than, that of typical local learners which require the whole training dataset The set of experiments conducted using several datasets indicates accuracy comparable to that of local learners, while no participating learner observes the whole training set, preserving both privacy and communication bandwidth.

关键词： horizontally distributed data distributed classification

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distributed INFERENCE OVER REGRESSION AND classification MODELS

DISTRIBUTED INFERENCE OVER REGRESSION AND CLASSIFICATION MOD...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Towfic, Zaid J. Chen, Jianshu Sayed, Ali H. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

ISBN: (纸本)9781479903566

We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the rate 1/Ni on average and with high probability, where N is the number of nodes in the network and i is the number of iterations. We derive asymptotic expressions for the expected regularized KL divergence and show that the diffusion strategy can outperform both non-cooperative and conventional centralized strategies, since diffusion implementations can weigh a node's contribution in proportion to its noise level.

关键词： distributed regression distributed classification diffusion adaptation Kullback-Leibler divergence relative entropy

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Multiuser Automatic Modulation classification for Cognitive Radios using distributed Sensing in Multipath Fading Channels

Multiuser Automatic Modulation Classification for Cognitive ...

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7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM)

作者： Sohul, Munawwar M. Ramkumar, Barathram Bose, Tamal Virginia Polytech Inst & State Univ Bradley Dept Elect & Comp Engn Wireless Virginia Tech Blacksburg VA 24060 USA IIT Mandi Sch Elect & Comp Engn Kataula India

ISBN: (纸本)9781936968558

Automatic Modulation Classifier (AMC) is an important component of a Cognitive Radio (CR) architecture that helps in better utilization of the spectrum. The successful implementation and employment of various cognitive radio services are largely dependent on the modulation classification performance of the cognitive radio terminals. AMC in literature is mostly developed for classifying the signal transmitted by a single user. Multiuser AMC, as the name suggests, simultaneously classifies signals transmitted by multiple users, while existing approaches rely on centralized processing. In this paper, we propose a fourth order cumulant based multiuser distributed AMC that can perform well even in a multipath fading environment. The proposed algorithm uses spatially distributed CRs cooperating with each other, which results in improved classification performance. Simulation results are provided to illustrate the promising results yielded by the proposed algorithm.

关键词： Cognitive radio multiuser AMC distributed classification higher order cumulants multipath fading

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Fast classification Algorithms via distributed Accelerated Alternating Direction Method of Multipliers 19

Fast Classification Algorithms via Distributed Accelerated A...

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19th IEEE International Conference on Data Mining (ICDM)

作者： Wang, Huihui Meng, Shunmei Qiao, Yiming Zhang, Jing Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Software Sch Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728146034

distributed machine learning has gained lots of attention due to the rapid growth of data. In this paper, we focus regularized empirical risk minimization problems, and propose two novel distributed Accelerated Alternating Direction Method of Multipliers (D-A2DM2) algorithms for distributed classification. Based on the framework of Alternating Direction Method of Multipliers (ADMM), we decentralize the distributed classification problem as a global consensus optimization problem with a series of sub-problems. In D-A2DM2, we exploit ADMM with variance reduction for sub-problem optimization in parallel. Taking global update and local update into consideration respectively, we propose two acceleration mechanisms in the framework of D-A2DM2. In particular, inspired by Nesterov's accelerated gradient descent, we utilize it for global update to further improve time efficiency. Moreover, we also introduce Nesterov's acceleration for local update, and develop the corrected local update and symmetric dual update to accelerate the convergence with only a little change in the computational effort. Theoretically, D-A2DM2 has a linear convergence rate. Empirically, experimental results show that D-A2DM2 converge faster than existing distributed ADMM-based classification, and could be a highly efficient algorithm for practical use.

关键词： ADMM Nesterov's Acceleration distributed classification Accelerated ADMM

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Robust and computationally efficient diffusion-based classification in distributed networks 40

Robust and computationally efficient diffusion-based classif...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Binder, Patricia Muma, Michael Zoubir, Abdelhak M. Signal Processing Group Technische Universität Darmstadt Darmstadt Germany

ISBN: (纸本)9781467369978

Today's wireless sensor networks provide the possibility to monitor physical environments via small low-cost wireless devices. Given the large amount of sensed data, efficient and robust classification becomes a critical task in many applications. Typically, the devices must operate under stringent power and communication constraints and the transmission of observations to a fusion center (FC) is, in many cases, infeasible or undesired. A challenging research question in such cases is the design of data clustering and classification rules when each sensor collects a set of unlabelled observations that are drawn from a known number of classes. We propose two robust distributed hybrid classification algorithms, i.e., the Diffusion K-Medians and the Communicationally Efficient distributed K-Medians. An extensive performance analysis in comparison to a benchmark algorithm is provided that investigates the error rates in dependence of different parameters of a distributed sensor network, and also considers communication cost. Our proposed algorithms, which are insensitive to outliers and various parameters, are applicable to on-line classification problems and scale well w.r.t. the number of classes. © 2015 IEEE.

关键词： diffusion distributed classification K-medians outlier robust

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Supervised classification of distributed data streams for smart grids

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2012年第1期3卷 95-108页

作者： Guarracino, Mario R. Irpino, Antonio Radziukyniene, Neringa Verde, Rosanna CNR High Performance Comp & Networking Naples Italy Univ Napoli Dipartimento Studi Europei & Mediterranei Caserta Italy Lithuanian Energy Inst Lab Syst Control & Automat Kaunas Lithuania

The electricity system inherited from the 19th and 20th centuries has been a reliable but centralized system. With the spreading of local, distributed and intermittent renewable energy resources, top-down central control of the grid no longer meets modern requirements. For these reasons, the power grid has been equipped with smart meters integrating bi-directional communications, advanced power measurement and management capabilities. Smart meters make it possible to remotely turn power on or off to a customer, read usage information, detect a service outage and the unauthorized use of electricity. To fully exploit their capabilities, we foresee the usage of distributed supervised classification algorithms. By gathering data available from meters and other sensors, such algorithms can create local classification models for attack detection, online monitoring, privacy preservation, workload balancing, prediction of energy demand and incoming faults. In this paper we present a decentralized distributed classification algorithm based on proximal support vector machines. The method uses partial knowledge, in form of data streams, to build its local model on each meter. We demonstrate the performance of the proposed scheme on synthetic datasets.

关键词： Supervised learning distributed classification Generalized eigenvalue classifier Smart grids

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Robust and computationally efficient diffusion-based classification in distributed networks

Robust and computationally efficient diffusion-based classif...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： P. Binder M. Muma A. M. Zoubir Signal Process. Group Tech. Univ. Darmstadt Darmstadt Germany

ISBN: (纸本)9781467369985

关键词： K-medians diffusion distributed classification outlier robust outliers Robustness diffusion Error analysis data clustering Distribute distributed NETWORKS Communication Research Sensor networks

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