5,135 research outputs found
An Event-based Diffusion LMS Strategy
We consider a wireless sensor network consists of cooperative nodes, each of
them keep adapting to streaming data to perform a least-mean-squares
estimation, and also maintain information exchange among neighboring nodes in
order to improve performance. For the sake of reducing communication overhead,
prolonging batter life while preserving the benefits of diffusion cooperation,
we propose an energy-efficient diffusion strategy that adopts an event-based
communication mechanism, which allow nodes to cooperate with neighbors only
when necessary. We also study the performance of the proposed algorithm, and
show that its network mean error and MSD are bounded in steady state. Numerical
results demonstrate that the proposed method can effectively reduce the network
energy consumption without sacrificing steady-state network MSD performance
significantly
Diffusion Adaptation Strategies for Distributed Estimation over Gaussian Markov Random Fields
The aim of this paper is to propose diffusion strategies for distributed
estimation over adaptive networks, assuming the presence of spatially
correlated measurements distributed according to a Gaussian Markov random field
(GMRF) model. The proposed methods incorporate prior information about the
statistical dependency among observations, while at the same time processing
data in real-time and in a fully decentralized manner. A detailed mean-square
analysis is carried out in order to prove stability and evaluate the
steady-state performance of the proposed strategies. Finally, we also
illustrate how the proposed techniques can be easily extended in order to
incorporate thresholding operators for sparsity recovery applications.
Numerical results show the potential advantages of using such techniques for
distributed learning in adaptive networks deployed over GMRF.Comment: Submitted to IEEE Transactions on Signal Processing. arXiv admin
note: text overlap with arXiv:1206.309
Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View
Distributed adaptive filtering has been considered as an effective approach
for data processing and estimation over distributed networks. Most existing
distributed adaptive filtering algorithms focus on designing different
information diffusion rules, regardless of the nature evolutionary
characteristic of a distributed network. In this paper, we study the adaptive
network from the game theoretic perspective and formulate the distributed
adaptive filtering problem as a graphical evolutionary game. With the proposed
formulation, the nodes in the network are regarded as players and the local
combiner of estimation information from different neighbors is regarded as
different strategies selection. We show that this graphical evolutionary game
framework is very general and can unify the existing adaptive network
algorithms. Based on this framework, as examples, we further propose two
error-aware adaptive filtering algorithms. Moreover, we use graphical
evolutionary game theory to analyze the information diffusion process over the
adaptive networks and evolutionarily stable strategy of the system. Finally,
simulation results are shown to verify the effectiveness of our analysis and
proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin
Distributed Local Linear Parameter Estimation using Gaussian SPAWN
We consider the problem of estimating local sensor parameters, where the
local parameters and sensor observations are related through linear stochastic
models. Sensors exchange messages and cooperate with each other to estimate
their own local parameters iteratively. We study the Gaussian Sum-Product
Algorithm over a Wireless Network (gSPAWN) procedure, which is based on belief
propagation, but uses fixed size broadcast messages at each sensor instead.
Compared with the popular diffusion strategies for performing network parameter
estimation, whose communication cost at each sensor increases with increasing
network density, the gSPAWN algorithm allows sensors to broadcast a message
whose size does not depend on the network size or density, making it more
suitable for applications in wireless sensor networks. We show that the gSPAWN
algorithm converges in mean and has mean-square stability under some technical
sufficient conditions, and we describe an application of the gSPAWN algorithm
to a network localization problem in non-line-of-sight environments. Numerical
results suggest that gSPAWN converges much faster in general than the diffusion
method, and has lower communication costs, with comparable root mean square
errors
Decentralized Clustering and Linking by Networked Agents
We consider the problem of decentralized clustering and estimation over
multi-task networks, where agents infer and track different models of interest.
The agents do not know beforehand which model is generating their own data.
They also do not know which agents in their neighborhood belong to the same
cluster. We propose a decentralized clustering algorithm aimed at identifying
and forming clusters of agents of similar objectives, and at guiding
cooperation to enhance the inference performance. One key feature of the
proposed technique is the integration of the learning and clustering tasks into
a single strategy. We analyze the performance of the procedure and show that
the error probabilities of types I and II decay exponentially to zero with the
step-size parameter. While links between agents following different objectives
are ignored in the clustering process, we nevertheless show how to exploit
these links to relay critical information across the network for enhanced
performance. Simulation results illustrate the performance of the proposed
method in comparison to other useful techniques
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