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IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet)

作者： Alkhweldi, Marwan West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781509034611

This paper studies the problem of distributed parameter estimation in wireless sensor network under energy constraints. Optimization formulas that guarantee the best estimation performance from the available energy are derived. The network consists of sensors that are deployed over an area at random. Sensors' observations are noisy measurements of an underlying field. Sensors have limited energy for the transmission process. Each sensor processes its observation prior to transmitting it to a fusion center, where a field parameter vector is estimated. Transmission channels between the sensors and the fusion center are assumed to be noisy parallel channels. The sensors' locations, the noise probability density function, and the field characteristic function are assumed to be known at the fusion center. This work presents two strategies that can be followed for optimal energy allocation:(1) Minimizing Cramer-Rao Lower Bound of the estimates with respect to energy allocation (2) Minimizing the sensors' observations transmission error with respect to energy allocation. Simulation results which support the optimization formulas are shown.

关键词： Wireless sensor network maximum-likelihood estimation distributed parameter estimation mean squared error energy constraint

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Linear Weighting Method And Calibration Technique With Application For Temperature estimation In Inter Weather Stations Regions 21

Linear Weighting Method And Calibration Technique With Appli...

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21st International Conference on System Theory, Control and Computing (ICSTCC)

作者： Petrila, Iulian Chiriac, Ciprian Manta, Vasile Gheorghe Asachi Tech Univ Iasi Fac Automat Control & Comp Engn Dept Comp Sci Str Dimitrie Mangeron 27 Iasi 700050 Romania

ISBN: (纸本)9781538638422

A new linear weighting method and a calibration technique tested comparatively to the proposed linear method and to inverse distance weighting method are analyzed. The proposed methods are tested for temperature estimation in inter weather stations regions, with cumulative minimum deviations from the real values. The analysis reveals that for some weather stations configurations, with certain statistical characteristics, the linear method is more efficient than the optimal parameterized inverse distance weighting method. The methods can be also used in the estimation of other spatially distributed parameters.

关键词： interpolation methods distributed parameter estimation calibration techniques weighting methods distributed systems inverse distance weighting temperature estimation methods

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Cyber attacks on estimation sensor networks and iots: Impact, mitigation and implications to unattacked systems

Cyber attacks on estimation sensor networks and iots: Impact...

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2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

作者： Zhang, Jiangfan Blum, Rick S. Kaplan, Lance EE Department Columbia University New YorkNY10027 United States ECE Department Lehigh University BethlehemPA18015 United States U.S. Army Research Lab. 2800 Powder Mill Road AdelphiMD20783 United States

ISBN: (纸本)9781509041176

estimation of an unknown deterministic vector from quantized sensor data is considered in the presence of spoofing and man-in-the-middle attacks. First, asymptotically optimum processing, which identifies and categorizes the attacked sensors into different groups according to distinct types of attacks, is outlined in the face of man-in-the-middle attacks. Necessary and sufficient conditions are provided under which utilizing the attacked sensor data will lead to better estimation performance when compared to approaches where the attacked sensors are ignored. Next, necessary and sufficient conditions are provided under which spoofing attacks provide a guaranteed attack performance in terms of the Cramer-Rao Bound regardless of the processing the estimation system employs. It is shown that it is always possible to construct such a highly desirable attack by properly employing an attack vector parameter having a sufficiently large dimension relative to the number of quantization levels employed, which was not observed previously. For unattacked quantized estimation systems, a general limitation on the dimension of a vector parameter which can be accurately estimated is uncovered. © 2017 IEEE.

关键词： Cramer-Rao Bound distributed parameter estimation man-in-the-middle attack sensor network spoofing attack

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distributed estimation over sensor networks based on distributed conjugate gradient strategies

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IET SIGNAL PROCESSING 2016年第3期10卷 291-301页

作者： Xu, Songcen de Lamare, Rodrigo C. Poor, H. Vincent Univ York Dept Elect Commun & Signal Proc Res Grp York YO10 5DD N Yorkshire England CETUC PUC Rio Rio De Janeiro Brazil Univ York Dept Elect York YO10 5DD N Yorkshire England Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

This study presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional CG (CCG) and modified CG (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least-mean square-based algorithms and a performance that is close to recursive least-squares algorithms. In comparison with existing centralised or distributed estimation strategies, key features of the proposed algorithms are: (i) more accurate estimates and faster convergence speed can be obtained and (ii) the design of preconditioners for CG algorithms, which can improve the performance of the proposed CG algorithms is presented. Simulations show the performance of the proposed CG algorithms against previously reported techniques for distributed parameter estimation and distributed spectrum estimation applications.

关键词： wireless sensor networks gradient methods distributed algorithms spread spectrum communication mean square error methods distributed estimation sensor networks distributed conjugate gradient algorithm mean square error distributed parameter estimation distributed spectrum estimation

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Diffusion LMS Strategies in Sensor Networks With Noisy Input Data

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IEEE-ACM TRANSACTIONS ON NETWORKING 2016年第1期24卷 3-14页

作者： Abdolee, Reza Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from consideration. We propose a bias-elimination technique and develop a novel class of diffusion LMS algorithms that can mitigate the effect of regression noise and obtain an unbiased estimate of the unknown parameter vector over the network. In our development, we first assume that the variances of the regression noises are known a priori. Later, we relax this assumption by estimating these variances in real time. We analyze the stability and convergence of the proposed algorithms and derive closed-form expressions to characterize their mean-square error performance in transient and steady-state regimes. We further provide computer experiment results that illustrate the efficiency of the proposed algorithms and support the analytical findings.

关键词： Bias-compensated least-mean square (LMS) diffusion adaptation distributed parameter estimation network optimization

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Cyber attacks on estimation sensor networks and iots: Impact, mitigation and implications to unattacked systems

Cyber attacks on estimation sensor networks and iots: Impact...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jiangfan Zhang Rick S. Blum Lance Kaplan EE department Columbia University New York 10027 USA ECE department Lehigh University Bethlehem PA 18015 USA U.S. Army Research Lab. 2800 Powder Mill Road Adelphi MD 20783 USA

ISBN: (纸本)9781509041183

关键词： distributed parameter estimation man-in-the-middle attack spoofing attack Cramer-Rao Bound sensor network Spoofing Sensor networks Cramer-Rao bounds quantization level Sensor Data parameter estimation Sufficient conditions computer information security Information warfare Necessary

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distributed Bias-Compensated Recursive Least Squares estimation over Multi-Agent Networks 35

Distributed Bias-Compensated Recursive Least Squares Estimat...

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第35届中国控制会议

作者： LOU Jian JIA Lijuan YE Yangzhi YANG Zijiang Department of Electronic Engineering Beijing Institute of Technology Department of Intelligent Systems Engineering Ibaraki University 4-12-1 Nakanarusawa

ISBN: (纸本)9781509009107

We study the problem of distributed parameter estimation over multi-agent networks,where agents collaborate to estimate a common parameter *** the situation where both the input and the output of each agent are corrupted by additive noise,traditional recursive least-squares(RLS) algorithms are *** stand-alone bias-compensated RLS(BCRLS)algorithm can remove the effect of noise-induced bias but increases the variance of the local *** has been discussed that the variance can be significantly reduced by distributed ***,a series of distributed BCRLS algorithms are proposed based on current mainstream distributed cooperation schemes(incremental,consensus and diffusion schemes).And performance of the proposed distributed algorithms is *** results show that distributed BCRLS estimation performs better than the noncooperative BCRLS(Nco-BCRLS) *** the diffusion BCRLS(Diff-BCRLS) algorithm has the best performance in estimation accuracy and tracking capability among the distributed algorithms.

关键词： distributed parameter estimation Multi-Agent Networks distributed Strategies Bias Compensation LeastSquares

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Optimal Sensor Density and Placement in Power-constrained Wireless Sensor Networks

Optimal Sensor Density and Placement in Power-constrained Wi...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Alkhweld, Marwan West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781509045884

This paper presents algorithms that find the optimal number of sensor nodes and the optimal location of nodes to achieve more efficient estimation in a wireless sensor network under power constraints. The network consists of sensors that are optimally placed over an area. Sensors' observations are noisy measurements of an underlying field. Each sensor processes its observation prior to transmitting it to a fusion center, where a field parameter vector is estimated. The network has limited power for the observations transmission processes. Transmission channels between the sensors and the fusion center are assumed to be noisy parallel channels. The sensors' locations, the noise probability density function, and the field characteristic function are assumed to be known at the fusion center. Simulation results which support the derived algorithms are shown.

关键词： Wireless sensor network maximum-likelihood estimation sensor placement distributed parameter estimation Fisher information

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Sequential estimation of Mixtures in Diffusion Networks

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IEEE SIGNAL PROCESSING LETTERS 2015年第2期22卷 197-201页

作者： Dedecius, Kamil Reichl, Jan Djuric, Petar M. Acad Sci Czech Republ Inst Informat Theory & Automat CR-18208 Prague 8 Czech Republic SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

The letter studies the problem of sequential estimation of mixtures in diffusion networks whose nodes communicate only with their adjacent neighbors. The adopted quasi-Bayesian approach yields a probabilistically consistent and computationally non-intensive and fast method, applicable to awide class of mixture models with unknown component parameters and weights. Moreover, if conjugate priors are used for inferring the component parameters, the solution attains a closed analytic form.

关键词： Diffusion distributed parameter estimation sensor networks sequential mixture estimation

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distributed Sparsity-Aware Sensor Selection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第22期63卷 5951-5964页

作者： Jamali-Rad, Hadi Simonetto, Andrea Ma, Xiaoli Leus, Geert Delft Univ Technol Fac EEMCS NL-2628 CD Delft Netherlands Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. The problem becomes even more interesting in a distributed configuration when each sensor has to decide itself whether it should contribute to the estimation or not. In this paper, we explore the sparsity embedded within the problem and propose a sparsity-aware sensor selection paradigm for both uncorrelated and correlated noise experienced at different sensors. We also present reasonably low-complexity and elegant distributed algorithms in order to solve the centralized problems with convergence guarantees within a bounded error. Furthermore, we analytically quantify the complexity of the distributed algorithms compared to centralized ones. Our simulation results corroborate our claims and illustrate a promising performance for the proposed centralized and distributed algorithms.

关键词： distributed parameter estimation sensor selection sparsity

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