9,141 research outputs found
Distributed parameter and state estimation for wireless sensor networks
The research in distributed algorithms is linked with the developments of statistical inference
in wireless sensor networks (WSNs) applications. Typically, distributed approaches process
the collected signals from networked sensor nodes. That is to say, the sensors receive local
observations and transmit information between each other. Each sensor is capable of combining
the collected information with its own observations to improve performance. In this thesis, we
propose novel distributed methods for the inference applications using wireless sensor networks.
In particular, the efficient algorithms which are not computationally intensive are investigated.
Moreover, we present a number of novel algorithms for processing asynchronous network events
and robust state estimation.
In the first part of the thesis, a distributed adaptive algorithm based on the component-wise
EM method for decentralized sensor networks is investigated. The distributed component-wise
Expectation-Maximization (EM) algorithm has been designed for application in a Gaussian
density estimation. The proposed algorithm operates a component-wise EM procedure for local
parameter estimation and exploit an incremental strategy for network updating, which can provide
an improved convergence rate. Numerical simulation results have illustrated the advantages of
the proposed distributed component-wise EM algorithm for both well-separated and overlapped
mixture densities. The distributed component-wise EM algorithm can outperform other EM-based
distributed algorithms in estimating overlapping Gaussian mixtures.
In the second part of the thesis, a diffusion based EM gradient algorithm for density estimation
in asynchronous wireless sensor networks has been proposed. Specifically, based on the
asynchronous adapt-then-combine diffusion strategy, a distributed EM gradient algorithm that
can deal with asynchronous network events has been considered. The Bernoulli model has been
exploited to approximate the asynchronous behaviour of the network. Compared with existing
distributed EM based estimation methods using a consensus strategy, the proposed algorithm
can provide more accurate estimates in the presence of asynchronous networks uncertainties,
such as random link failures, random data arrival times, and turning on or off sensor nodes
for energy conservation. Simulation experiments have been demonstrated that the proposed
algorithm significantly outperforms the consensus based strategies in terms of Mean-Square-
Deviation (MSD) performance in an asynchronous network setting.
Finally, the challenge of distributed state estimation in power systems which requires low
complexity and high stability in the presence of bad data for a large scale network is addressed.
A gossip based quasi-Newton algorithm has been proposed for solving the power system state
estimation problem. In particular, we have applied the quasi-Newton method for distributed
state estimation under the gossip protocol. The proposed algorithm exploits the Broyden-
Fletcher-Goldfarb-Shanno (BFGS) formula to approximate the Hessian matrix, thus avoiding the
computation of inverse Hessian matrices for each control area. The simulation results for IEEE
14 bus system and a large scale 4200 bus system have shown that the distributed quasi-Newton
scheme outperforms existing algorithms in terms of Mean-Square-Error (MSE) performance with
bad data
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
Robust Distributed Parameter Estimation in Wireless Sensor Networks
abstract: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Renyi Entropy based Target Tracking in Mobile Sensor Networks
This paper proposes an entropy based target tracking approach for mobile sensor networks. The proposed tracking algorithm runs a target state estimation stage and a motion control stage alternatively. A distributed particle filter is developed to estimate the target position in the first stage. This distributed particle filter does not require to transmit the weighted particles from one sensor node to another. Instead, a Gaussian mixture model is formulated to approximate the posterior distribution represented by the weighted particles via an EM algorithm. The EM algorithm is developed in a distributed form to compute the parameters of Gaussian mixture model via local communication, which leads to the distributed implementation of the particle filter. A flocking controller is developed to control the mobile sensor nodes to track the target in the second stage. The flocking control algorithm includes three components. Collision avoidance component is based on the design of a separation potential function. Alignment component is based on a consensus algorithm. Navigation component is based on the minimization of an quadratic Renyi entropy. The quadratic Renyi entropy of Gaussian mixture model has an analytical expression so that its optimization is feasible in mobile sensor networks. The proposed active tracking algorithm is tested in simulation. © 2011 IFAC
Distributed Diffusion-based LMS for Node-Specific Parameter Estimation over Adaptive Networks
A distributed adaptive algorithm is proposed to solve a node-specific
parameter estimation problem where nodes are interested in estimating
parameters of local interest and parameters of global interest to the whole
network. To address the different node-specific parameter estimation problems,
this novel algorithm relies on a diffusion-based implementation of different
Least Mean Squares (LMS) algorithms, each associated with the estimation of a
specific set of local or global parameters. Although all the different LMS
algorithms are coupled, the diffusion-based implementation of each LMS
algorithm is exclusively undertaken by the nodes of the network interested in a
specific set of local or global parameters. To illustrate the effectiveness of
the proposed technique we provide simulation results in the context of
cooperative spectrum sensing in cognitive radio networks.Comment: 5 pages, 2 figures, Published in Proc. IEEE ICASSP, Florence, Italy,
May 201
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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