866 research outputs found
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
Multitask Diffusion Adaptation over Networks
Adaptive networks are suitable for decentralized inference tasks, e.g., to
monitor complex natural phenomena. Recent research works have intensively
studied distributed optimization problems in the case where the nodes have to
estimate a single optimum parameter vector collaboratively. However, there are
many important applications that are multitask-oriented in the sense that there
are multiple optimum parameter vectors to be inferred simultaneously, in a
collaborative manner, over the area covered by the network. In this paper, we
employ diffusion strategies to develop distributed algorithms that address
multitask problems by minimizing an appropriate mean-square error criterion
with -regularization. The stability and convergence of the algorithm in
the mean and in the mean-square sense is analyzed. Simulations are conducted to
verify the theoretical findings, and to illustrate how the distributed strategy
can be used in several useful applications related to spectral sensing, target
localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio
A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation
We consider a multitask estimation problem where nodes in a network are
divided into several connected clusters, with each cluster performing a
least-mean-squares estimation of a different random parameter vector. Inspired
by the adapt-then-combine diffusion strategy, we propose a multitask diffusion
strategy whose mean stability can be ensured whenever individual nodes are
stable in the mean, regardless of the inter-cluster cooperation weights. In
addition, the proposed strategy is able to achieve an asymptotically unbiased
estimation, when the parameters have same mean. We also develop an
inter-cluster cooperation weights selection scheme that allows each node in the
network to locally optimize its inter-cluster cooperation weights. Numerical
results demonstrate that our approach leads to a lower average steady-state
network mean-square deviation, compared with using weights selected by various
other commonly adopted methods in the literature.Comment: 30 pages, 8 figures, submitted to IEEE Journal of Selected Topics in
Signal Processin
Attack-resistant location estimation in wireless sensor networks
Many sensor network applications require sensors’ locations to function correctly. Despite the recent advances, location discovery for sensor networks in hostile environments has been mostly overlooked. Most of the existing localization protocols for sensor networks are vulnerable in hostile environments. The security of location discovery can certainly be enhanced by authentication. However, the possible node compromises and the fact that location determination uses certain physical features (e.g., received signal strength) of radio signals make authentication not as effective as in traditional security applications. This paper presents two methods to tolerate malicious attacks against range-based location discovery in sensor networks. The first method filters out malicious beacon signals on the basis of the “consistency” among multiple beacon signals, while the second method tolerates malicious beacon signals by adopting an iteratively refined voting scheme. Both methods can survive malicious attacks even if the attacks bypass authentication, provided that the benign beacon signals constitute the majority of the beacon signals. This paper also presents the implementation and experimental evaluation (through both field experiments and simulation) of all the secure and resilient location estimation schemes that can be used on the current generation of sensor platforms (e.g., MICA series of motes), including the techniques proposed in this paper, in a network of MICAz motes. The experimental results demonstrate the effectiveness of the proposed methods, and also give the secure and resilient location estimation scheme most suitable for the current generation of sensor networks
Diffusion lms strategy over wireless sensor network
The mess with distributed detection, where nodes arranged in certain topology are obliged to decideamong two speculations focused around accessible estimations.We look for completely appropriated and versatile usage, where all nodes make singular constant-choices by putting crosswise over with their quick neighbours just, and no combination focus is vital. The proffered distributed detection algorithms are based on a concept of extension of strategies that are employed for diffusion mechanism in a distributed network topology. After a large-scale systematic plan or arrangement for attaining some particular object or putting a particular idea into effect detection using diffusion LMS are fascinating in the context of sensor networksbecause of their versatility, enhanced strength to node and connection disappointment as contrasted with unified frameworks and their capability to convey vitality and correspondence assets. The proposed algorithms are inherently adaptive and can track changes in the element speculation.We examine the operation of the suggested algorithms in terms of their chances of detection and false alarm, and provide simulation results comparing with other cooperation schemes, including centralized processing and the case where there is no cooperation. In the context of digital signal processing and communication, the role of adaptive filters is very vital. In day to daywork where practical requirement is necessary,the computational complexities is the most considerable parameter in context of an adaptive filter. As it tells us about reliability of any system, agility to real time environment least mean squares (LMS) algorithm is generally utilized in light of its low computational multifaceted nature (O(N)) and easier in implementation.
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