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distributed parameter estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication

IEEE TRANSACTIONS ON INFORMATION THEORY 2012年第6期58卷 3575-3605页

作者： Kar, Soummya Moura, Jose M. F. Ramanan, Kavita Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Brown Univ Div Appl Math Providence RI 02912 USA

The paper studies distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and noisy intersensor communication. It introduces separably estimable observation models that generalize the observability condition in linear centralized estimation to nonlinear distributed estimation. It studies two distributed estimation algorithms in separably estimable models, the NU (with its linear counterpart LU) and the NLU. Their update rule combines a consensus step (where each sensor updates the state by weight averaging it with its neighbors' states) and an innovation step (where each sensor processes its local current observation). This makes the three algorithms of the consensus + innovations type, very different from traditional consensus. This paper proves consistency (all sensors reach consensus almost surely and converge to the true parameter value), efficiency, and asymptotic unbiasedness. For LU and NU, it proves asymptotic normality and provides convergence rate guarantees. The three algorithms are characterized by appropriately chosen decaying weight sequences. Algorithms LU and NU are analyzed in the framework of stochastic approximation theory;algorithm NLU exhibits mixed time-scale behavior and biased perturbations, and its analysis requires a different approach that is developed in this paper.

关键词： Asymptotic normality consensus consensus plus innovations consistency distributed parameter estimation Laplacian separable estimable spectral graph theory stochastic approximation unbiasedness

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Privacy-Preserving distributed estimation Over Multitask Networks

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 1953-1965页

作者： Shen, Xicong Liu, Ying Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

distributed estimation over multitask networks, where the target parameter vectors (tasks) for different nodes can be different, has received much attention recently. In this article, we consider some practical application scenarios, where there are some similarities between tasks, and thus, intertask cooperation is beneficial to improve the estimation performance of nodes. In most of the existing multitask learning studies, local estimates are directly transmitted between neighboring nodes, and then, the adaptive combination strategy is adopted to achieve intertask cooperation. However, when the target parameter vectors contain sensitive information, direct transmission of local estimates may result in serious privacy breaches. To tackle this problem, we propose a privacy-preserving distributed multitask learning algorithm for collaborative estimation over networks. The proposed algorithm is implemented by a secure multiparty computation protocol designed on an organic combination of multiplicative/additive mask and additively homomorphic encryption technique. While allowing each node to adaptively cooperate with its neighbors, this protocol also preserves the privacy of the local estimates. Besides, we present a thorough privacy analysis of the proposed algorithm. Simulation results show that the proposed algorithm can effectively protect each node's task against leakage without sacrificing the performance of estimation.

关键词： estimation Task analysis Protocols Collaboration Simulation Distance learning Computer aided instruction distributed parameter estimation homomorphic encryption (HE) multitask networks privacy preserving secure multiparty computation (SMC)

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distributed parameter estimation in wireless sensor networks using fused local observations

Distributed parameter estimation in wireless sensor networks...

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Conference on Wireless Sensing, Localization and Processing VII

作者： Fanaei, Mohammad Valenti, Matthew C. Schmid, Natalia A. Alkhweldi, Marwan M. W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9780819490827

The goal of this paper is to reliably estimate a vector of unknown deterministic parameters associated with an underlying function at a fusion center of a wireless sensor network based on its noisy samples made at distributed local sensors. A set of noisy samples of a deterministic function characterized by a finite set of unknown parameters to be estimated is observed by distributed sensors. The parameters to be estimated can be some attributes associated with the underlying function, such as its height, its center, its variances in different directions, or even the weights of its specific components over a predefined basis set. Each local sensor processes its observation and sends its processed sample to a fusion center through parallel impaired communication channels. Two local processing schemes, namely analog and digital, are considered. In the analog local processing scheme, each sensor transmits an amplified version of its local analog noisy observation to the fusion center, acting like a relay in a wireless network. In the digital local processing scheme, each sensor quantizes its noisy observation before transmitting it to the fusion center. A flat-fading channel model is considered between the local sensors and fusion center. The fusion center combines all of the received locally-processed observations and estimates the vector of unknown parameters of the underlying function. Two different well-known estimation techniques, namely maximum-likelihood (ML), for both analog and digital local processing schemes, and expectation maximization (EM), for digital local processing scheme, are considered at the fusion center. The performance of the proposed distributed parameter estimation system is investigated through simulation of practical scenarios for a sample underlying function.

关键词： distributed parameter estimation maximum likelihood (ML) expectation maximization (EM) analog/digital local processing local quantization integrated mean-squared error flat-fading channel fusion center wireless sensor networks

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distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

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DIGITAL SIGNAL PROCESSING 2013年第4期23卷 1303-1313页

作者： Majhi, Babita Panda, Ganapati Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England GG Vishwavidyalaya Cent Univ Dept CSIT Bilaspur India Indian Inst Technol Sch Elect Sci Bhubaneswar Orissa India

In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark HR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers. (C) 2013 Elsevier Inc. All rights reserved.

关键词： distributed parameter estimation IIR system identification Particle swarm optimization Incremental particle swarm optimization (IPSO) Robust distributed parameter estimation

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distributed Recursive Projection Identification with Binary-Valued Observations

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Journal of Systems Science & Complexity 2021年第5期34卷 2048-2068页

作者： WANG Ying ZHAO Yanlong ZHANG Ji-Feng Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China.

This paper investigates a distributed recursive projection identification problem with binaryvalued observations built on a sensor network,where each sensor in the sensor network measures partial information of the unknown parameter only,but each sensor is allowed to communicate with its neighbors.A distributed recursive projection algorithm is proposed based on a specific projection operator and a diffusion *** authors establish the upper bound of the accumulated regrets of the adaptive predictor without any requirement of excitation ***,the convergence of the algorithm is given under the bounded cooperative excitation condition,which is more general than the previously imposed independence or persistent excitations on the system regressors and maybe the weakest one under binary observations.A numerical example is supplied to demonstrate the theoretical results and the cooperative effect of the sensors,which shows that the whole network can still fulfill the estimation task through exchanging information between sensors even if any individual sensor cannot.

关键词： Adaptive predictor binary-valued observations cooperative excitations distributed parameter estimation

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Sparse distributed learning based on diffusion minimum generalised rank norm

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IET SIGNAL PROCESSING 2020年第9期14卷 683-692页

作者： Modalavalasa, Sowjanya Sahoo, Upendra Kumar Sahoo, Ajit Kumar Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 Odisha India

The traditional least-squares based diffusion least mean squares is not robust against outliers present in either desired data or input data. The diffusion minimum generalised rank (GR) norm algorithm proposed in the earlier works of the authors was able to effectively estimate the parameter of interest in presence of outliers in both desired and input data. However, this manuscript deals with the robust distributed estimation over distributed networks exploiting sparsity underlying in the system model. The proposed algorithm is based on both GR norm and compressive sensing, where GR norm ensures robustness against outliers in input as well as desired data. The techniques from compressive sensing endow the network with adaptive learning of the sparse structure form the incoming data in real-time and it also enables tracking of the sparsity variations of the system model. The mean and mean square convergence of the proposed algorithm are analysed and the conditions under which the proposed algorithm outperforms the unregularised diffusion GR norm algorithm are also investigated. The proposed algorithms are validated for three different applications namely distributed parameter estimation, tracking and distributed power spectrum estimation.

关键词： estimation theory parameter estimation learning (artificial intelligence) adaptive signal processing compressed sensing diffusion minimum generalised rank norm algorithm robust distributed estimation distributed networks system model compressive sensing adaptive learning sparse structure form mean square convergence distributed parameter estimation sparse distributed learning diffusion minimum GR norm algorithm distributed power spectrum estimation

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INFERRING PRIVATE INFORMATION IN WIRELESS SENSOR NETWORKS 44

INFERRING PRIVATE INFORMATION IN WIRELESS SENSOR NETWORKS

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Burbano-L, Daniel A. George, Jemin Freeman, Randy A. Lynch, Kevin M. Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA US Army Res Lab Adelphi MD 20783 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

ISBN: (纸本)9781479981311

In wireless sensor networks, estimating a global parameter from locally obtained measurements via local interactions is known as the distributed parameter estimation problem. Solving these problems often require the deployment of distributed optimization algorithms that rely on a constant exchange of information among the sensor nodes. This makes such distributed algorithms vulnerable to attackers or malicious nodes that want to gain access to private information regarding the network. Based on the sliding mode control scheme, here we present a novel approach to infer sensitive information (e.g., gradient or private parameters of the local objective function) regarding a node of interest by intercepting the communication between the nodes. The effectiveness of the proposed approach is illustrated in a representative example of distributed event localization using an acoustic sensor network.

关键词： distributed parameter estimation wireless sensor network distributed optimization sliding control

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distributed bias-compensated normalized least-mean squares algorithms with noisy input

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Science China(Information Sciences) 2018年第11期61卷 193-207页

作者： Lu FAN Lijuan JIA Ran TAO Yue WANG School of Information and Electronics Beijing Institute of Technology

In this paper, we study the problem of distributed normalized least-mean squares(NLMS) estimation over multi-agent networks, where all nodes collaborate to estimate a common parameter of *** consider the situations that all nodes in the network are corrupted by both input and output noise. This yields into biased estimates by the distributed NLMS algorithms. In our analysis, we take all the noise into consideration and prove that the bias is dependent on the input noise variance. Therefore, we propose a bias compensation method to remove the noise-induced bias from the estimated results. In our development, we first assume that the variances of the input noise are known a priori and develop a series of distributed-based bias-compensated NLMS(BCNLMS) methods. Under various practical scenarios, the input noise variance is usually unknown a priori, therefore it is necessary to first estimate for its value before bias removal. Thus, we develop a real-time estimation method for the input noise variance, which overcomes the unknown property of this noise. Moreover, we perform some main analysis results of the proposed distributed BCNLMS algorithms. Furthermore, we illustrate the performance of the proposed distributed bias compensation method via graphical simulation results.

关键词： distributed parameter estimation normalized least-mean squares bias-compensated algorithms

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distributed incremental bias-compensated RLS estimation over multi-agent networks

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Science China(Information Sciences) 2017年第3期60卷 153-167页

作者： Jian LOU Lijuan JIA Ran TAO Yue WANG School of Information and Electronics Beijing Institute of Technology

In this paper, we study the problem of distributed bias-compensated recursive least-squares(BCRLS) estimation over multi-agent networks, where the agents collaborate to estimate a common parameter of interest. We consider the situation where both input and output of each agent are corrupted by unknown additive noise. Under this condition, traditional recursive least-squares(RLS) estimator is biased, and the bias is induced by the input noise variance. When the input noise variance is available, the effect of the noise-induced bias can be removed at the expense of an increase in estimation variance. Fortunately, it has been illustrated that distributed collaboration between agents can effectively reduce the variance and can improve the stability of the estimator. Therefore, a distributed incremental BC-RLS algorithm and its simplified version are proposed in this paper. The proposed algorithms can collaboratively obtain the estimates of the unknown input noise variance and remove the effect of the noise-induced bias. Then consistent estimation of the unknown parameter can be achieved in an incremental fashion. Simulation results show that the incremental BC-RLS solutions outperform existing solutions in some enlightening ways.

关键词： distributed parameter estimation bias compensation incremental schemes recursive least-squares multi-agent networks

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Variable forgetting factor mechanisms for diffusion recursive least squares algorithm in sensor networks

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2017年第1期2017卷 1页

作者： Zhang, Ling Cai, Yunlong Li, Chunguang de lamare, Rodrigo C. Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China CETUC PUC Rio Rio De Janeiro Brazil

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrum estimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions.

关键词： Sensor networks distributed parameter estimation distributed spectrum estimation Diffusion recursive least-squares Variable forgetting factor

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