13,325 research outputs found
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Distributed Estimation of a Parametric Field Using Sparse Noisy Data
The problem of distributed estimation of a parametric physical field is
stated as a maximum likelihood estimation problem. Sensor observations are
distorted by additive white Gaussian noise. Prior to data transmission, each
sensor quantizes its observation to levels. The quantized data are then
communicated over parallel additive white Gaussian channels to a fusion center
for a joint estimation. An iterative expectation-maximization (EM) algorithm to
estimate the unknown parameter is formulated, and its linearized version is
adopted for numerical analysis. The numerical examples are provided for the
case of the field modeled as a Gaussian bell. The dependence of the integrated
mean-square error on the number of quantization levels, the number of sensors
in the network and the SNR in observation and transmission channels is
analyzed.Comment: to appear at Milcom-201
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
On Distributed Linear Estimation With Observation Model Uncertainties
We consider distributed estimation of a Gaussian source in a heterogenous
bandwidth constrained sensor network, where the source is corrupted by
independent multiplicative and additive observation noises, with incomplete
statistical knowledge of the multiplicative noise. For multi-bit quantizers, we
derive the closed-form mean-square-error (MSE) expression for the linear
minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous
communication channels, we propose several rate allocation methods named as
longest root to leaf path, greedy and integer relaxation to (i) minimize the
MSE given a network bandwidth constraint, and (ii) minimize the required
network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao
lower bound (CRLB) and compare the MSE performance of our proposed methods
against the CRLB. Our results corroborate that, for low power multiplicative
observation noises and adequate network bandwidth, the gaps between the MSE of
our proposed methods and the CRLB are negligible, while the performance of
other methods like individual rate allocation and uniform is not satisfactory
Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation
This paper investigates the problem of distributed best linear unbiased
estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless
sensor network (WSN). In particular, the application of limited-feedback
strategies for the optimal power allocation in distributed estimation is
studied. In order to find the BLUE estimator of the unknown parameter, the FC
combines spatially distributed, linearly processed, noisy observations of local
sensors received through orthogonal channels corrupted by fading and additive
Gaussian noise. Most optimal power-allocation schemes proposed in the
literature require the feedback of the exact instantaneous channel state
information from the FC to local sensors. This paper proposes a
limited-feedback strategy in which the FC designs an optimal codebook
containing the optimal power-allocation vectors, in an iterative offline
process, based on the generalized Lloyd algorithm with modified distortion
functions. Upon observing a realization of the channel vector, the FC finds the
closest codeword to its corresponding optimal power-allocation vector and
broadcasts the index of the codeword. Each sensor will then transmit its analog
observations using its optimal quantized amplification gain. This approach
eliminates the requirement for infinite-rate digital feedback links and is
scalable, especially in large WSNs.Comment: 5 Pages, 3 Figures, 1 Algorithm, Forty Seventh Annual Asilomar
Conference on Signals, Systems, and Computers (ASILOMAR 2013
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