In this paper, we consider the problem of distributed parameter estimation in sensor networks. Each sensor makes successive observations of an unknown d-dimensional parameter, which might be subject to Gaussian random...
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In this paper, we consider the problem of distributed parameter estimation in sensor networks. Each sensor makes successive observations of an unknown d-dimensional parameter, which might be subject to Gaussian random noises. The sensors aim to infer the true value of the unknown parameter by cooperating with each other. To this end, we first generalize the so-called dynamic regressor extension and mixing (DREM) algorithm to stochastic systems, with which the problem of estimating a d-dimensional vector parameter is transformed to that ofd scalar ones: one for each of the unknown parameters. For each of the scalar problem, both combine-then-adapt (CTA) and adapt-then-combine (ATC) diffusion-based estimation algorithms are given, where each sensor performs a combination step to fuse the local estimates in its in-neighborhood, alongside an adaptation step to process its streaming observations. Under weak conditions on network topology and excitation of regressors, we show that the proposed estimators guarantee that each sensor infers the true parameter, even if any individual of them cannot by itself. Specifically, it is required that the union of topologies over an interval with fixed length is strongly connected. Moreover, the sensors must collectively satisfy a cooperative persistent excitation (PE) condition, which relaxes the traditional PE condition. Numerical examples are finally provided to illustrate the established results. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must o...
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Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must optimize their resource allocation to maximize the accuracy of parameterestimation. In this work, we consider a group of sensors or agents, each sampling from a different variable of a multivariate Gaussian distribution and having a different estimation objective. We formulate a sensor or agent's data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. This formulation captures a novel trade-off in energy use, between locally collecting univariate samples and collaborating to produce multivariate samples. When knowledge of the correlation between variables is available, we analytically identify two cases: (1) where the optimal data collection policy entails investing resources to transfer information for collaborative sampling, and (2) where knowledge of the correlation between samples cannot enhance estimation efficiency. When knowledge of certain correlations is unavailable, but collaboration remains potentially beneficial, we propose novel approaches that apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our sequential distributed parameter estimation problem. We illustrate the effectiveness of the proposed algorithms, DOUBLE-F, DOUBLE-Z, UCB-F, UCB-Z, through simulation.
Data privacy is an important issue in control systems,especially when datasets contain sensitive information about *** this paper,the authors are concerned with the differentially private distributedparameter estimat...
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Data privacy is an important issue in control systems,especially when datasets contain sensitive information about *** this paper,the authors are concerned with the differentially private distributed parameter estimation problem,that is,we estimate an unknown parameter while protecting the sensitive information of each ***,the authors propose a distributed stochastic approximation estimation algorithm in the form of the differentially private consensus+innovations(DP-CI),and establish the privacy and convergence property of the proposed ***,it is shown that the proposed algorithm asymptotically unbiased converges in mean-square to the unknown parameter while differential privacy-preserving holds for finite number of ***,the exponentially damping step-size and privacy noise for DP-CI algorithm is *** estimate approximately converges to the unknown parameter with an error proportional to the step-size parameter while differential privacy-preserving holds for all *** tradeoff between accuracy and privacy of the algorithm is effectively ***,a simulation example is provided to verify the effectiveness of the proposed algorithm.
Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and...
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Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the conditional probability distribution. Traditional parameterestimation methods for probability distributions require the collection of historical data of all WFs. However, in the context of multi-regional interconnected grids, neither regional ISOs nor WFs can collect the raw data of WFs in other regions due to privacy or competition considerations. Therefore, based on the Gaussian mixture model, this paper first proposes a privacy-preserving distributed expectation-maximization algorithm to estimate the parameters of the joint probability distribution. This algorithm consists of two original methods: (1) a privacy-preserving distributed summation algorithm and (2) a privacy-preserving distributed inner product algorithm. Then, we derive each WF's conditional probability distribution of forecast error from the joint one. By the proposed algorithms, WFs only need local calculations and privacy-preserving neighboring communications to achieve the whole parameterestimation. These algorithms are verified using the wind integration data set published by the NREL. (C) 2020 Elsevier Ltd. All rights reserved.
In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which mini...
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In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few "neighbors", furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations. Copyright (C) 2021 The Authors.
This paper concerns the difficult, somewhat circular, problem of managing complex interconnected dynamical systems. This generally must be done by avoiding complexity and the needs for fast fail-prone communications a...
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ISBN:
(纸本)9781728181929
This paper concerns the difficult, somewhat circular, problem of managing complex interconnected dynamical systems. This generally must be done by avoiding complexity and the needs for fast fail-prone communications and having only a limited number of measurements. We illustrate this problem on a typical complex energy management for cooling systems. We first derive conditions for decomposition into subsystems modeled in terms of their own internal dynamics and output measurements on the interfaces with other components. We then use these decomposed models to illustrate how well distributedestimation can be done. The accuracy of parameterestimation is demonstrated using a real-world commercial cooling system.
In this paper, we study a distributed parameter estimation problem with an asynchronous communication protocol over multi-agent systems. Different from traditional time-driven communication schemes, in this work, data...
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ISBN:
(纸本)9789881563972
In this paper, we study a distributed parameter estimation problem with an asynchronous communication protocol over multi-agent systems. Different from traditional time-driven communication schemes, in this work, data can be transmitted between agents intermittently rather than in a steady stream. First, we propose a recursive distributed estimator based on an event-triggered communication scheme, through which each agent can decide whether the current estimate is sent out to its neighbors or not. With this scheme, considerable communications between agents can be effectively reduced. Then, under mild conditions including a collective observability, we provide a design principle of triggering thresholds to guarantee the asymptotic unbiasedness and strong consistency. Furthermore, under certain conditions, we reveal that, with probability one, for every agent the time interval between two successive triggering instants goes to infinity as time goes to infinity. Finally, we provide a numerical simulation to validate the theoretical results of this paper.
A novelty of distributed parameter estimation strategy for a class of nonlinear system with a time-varying parameter is proposed in this paper. The approach relies upon the concepts of invariant manifold and cooperati...
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ISBN:
(数字)9781728198880
ISBN:
(纸本)9781728198897
A novelty of distributed parameter estimation strategy for a class of nonlinear system with a time-varying parameter is proposed in this paper. The approach relies upon the concepts of invariant manifold and cooperative persistent excitation condition, does not require a priori knowledge of the time-varying parameters. In addition, it is shown that the parameterestimation error is not only bounded but also can converge to a small neighborhood of the origin for sufficiently large value of gain. A numerical simulation example is presented to demonstrate the effectiveness of the proposed method.
In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which mini...
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In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbors”, furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations.
distributedestimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. The ambiguities are modelled by following a set-theoretic approach, that leads to each sensor employ...
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ISBN:
(纸本)9781479981311
distributedestimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. The ambiguities are modelled by following a set-theoretic approach, that leads to each sensor employing a non-convex constraint set on the parameter vector. Consensus can be used to reach an estimate consistent with the measurements of all nodes, assuming that such an estimate exists, but unfortunately, such an approach leads to a non-convex problem. Using proper assumptions, the considered problem is decomposed into two sub-problems, where one is well studied in literature and the other is modelled as a non-cooperative game. An exact potential function is derived for this game, and an algorithm for its solution is given. Numerical results, consistent with the theoretical findings, demonstrate the efficacy of the proposed approach.
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