11,251 research outputs found
Rate Allocation for Decentralized Detection in Wireless Sensor Networks
We consider the problem of decentralized detection where peripheral nodes
make noisy observations of a phenomenon and send quantized information about
the phenomenon towards a fusion center over a sum-rate constrained multiple
access channel. The fusion center then makes a decision about the state of the
phenomenon based on the aggregate received data. Using the Chernoff information
as a performance metric, Chamberland and Veeravalli previously studied the
structure of optimal rate allocation strategies for this scenario under the
assumption of an unlimited number of sensors. Our key contribution is to extend
these result to the case where there is a constraint on the maximum number of
active sensors. In particular, we find sufficient conditions under which the
uniform rate allocation is an optimal strategy, and then numerically verify
that these conditions are satisfied for some relevant sensor design rules under
a Gaussian observation model.Comment: Accepted at SPAWC 201
Channel Impulse Response-based Distributed Physical Layer Authentication
In this preliminary work, we study the problem of {\it distributed}
authentication in wireless networks. Specifically, we consider a system where
multiple Bob (sensor) nodes listen to a channel and report their {\it
correlated} measurements to a Fusion Center (FC) which makes the ultimate
authentication decision. For the feature-based authentication at the FC,
channel impulse response has been utilized as the device fingerprint.
Additionally, the {\it correlated} measurements by the Bob nodes allow us to
invoke Compressed sensing to significantly reduce the reporting overhead to the
FC. Numerical results show that: i) the detection performance of the FC is
superior to that of a single Bob-node, ii) compressed sensing leads to at least
overhead reduction on the reporting channel at the expense of a small
( dB) SNR margin to achieve the same detection performance.Comment: 6 pages, 5 figures, accepted for presentation at IEEE VTC 2017 Sprin
Decentralized sequential change detection using physical layer fusion
The problem of decentralized sequential detection with conditionally
independent observations is studied. The sensors form a star topology with a
central node called fusion center as the hub. The sensors make noisy
observations of a parameter that changes from an initial state to a final state
at a random time where the random change time has a geometric distribution. The
sensors amplify and forward the observations over a wireless Gaussian multiple
access channel and operate under either a power constraint or an energy
constraint. The optimal transmission strategy at each stage is shown to be the
one that maximizes a certain Ali-Silvey distance between the distributions for
the hypotheses before and after the change. Simulations demonstrate that the
proposed analog technique has lower detection delays when compared with
existing schemes. Simulations further demonstrate that the energy-constrained
formulation enables better use of the total available energy than the
power-constrained formulation in the change detection problem.Comment: 10 pages, two-column, 10 figures, revised based on feedback from
reviewers, accepted for publication in IEEE Trans. on Wireless Communication
Cores of Cooperative Games in Information Theory
Cores of cooperative games are ubiquitous in information theory, and arise
most frequently in the characterization of fundamental limits in various
scenarios involving multiple users. Examples include classical settings in
network information theory such as Slepian-Wolf source coding and multiple
access channels, classical settings in statistics such as robust hypothesis
testing, and new settings at the intersection of networking and statistics such
as distributed estimation problems for sensor networks. Cooperative game theory
allows one to understand aspects of all of these problems from a fresh and
unifying perspective that treats users as players in a game, sometimes leading
to new insights. At the heart of these analyses are fundamental dualities that
have been long studied in the context of cooperative games; for information
theoretic purposes, these are dualities between information inequalities on the
one hand and properties of rate, capacity or other resource allocation regions
on the other.Comment: 12 pages, published at
http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/318704 in EURASIP
Journal on Wireless Communications and Networking, Special Issue on "Theory
and Applications in Multiuser/Multiterminal Communications", April 200
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
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