11 research outputs found
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
Data-Efficient Quickest Outlying Sequence Detection in Sensor Networks
A sensor network is considered where at each sensor a sequence of random
variables is observed. At each time step, a processed version of the
observations is transmitted from the sensors to a common node called the fusion
center. At some unknown point in time the distribution of observations at an
unknown subset of the sensor nodes changes. The objective is to detect the
outlying sequences as quickly as possible, subject to constraints on the false
alarm rate, the cost of observations taken at each sensor, and the cost of
communication between the sensors and the fusion center. Minimax formulations
are proposed for the above problem and algorithms are proposed that are shown
to be asymptotically optimal for the proposed formulations, as the false alarm
rate goes to zero. It is also shown, via numerical studies, that the proposed
algorithms perform significantly better than those based on fractional
sampling, in which the classical algorithms from the literature are used and
the constraint on the cost of observations is met by using the outcome of a
sequence of biased coin tosses, independent of the observation process.Comment: Submitted to IEEE Transactions on Signal Processing, Nov 2014. arXiv
admin note: text overlap with arXiv:1408.474
Data-Efficient Quickest Change Detection with On-Off Observation Control
In this paper we extend the Shiryaev's quickest change detection formulation
by also accounting for the cost of observations used before the change point.
The observation cost is captured through the average number of observations
used in the detection process before the change occurs. The objective is to
select an on-off observation control policy, that decides whether or not to
take a given observation, along with the stopping time at which the change is
declared, so as to minimize the average detection delay, subject to constraints
on both the probability of false alarm and the observation cost. By considering
a Lagrangian relaxation of the constraint problem, and using dynamic
programming arguments, we obtain an \textit{a posteriori} probability based
two-threshold algorithm that is a generalized version of the classical Shiryaev
algorithm. We provide an asymptotic analysis of the two-threshold algorithm and
show that the algorithm is asymptotically optimal, i.e., the performance of the
two-threshold algorithm approaches that of the Shiryaev algorithm, for a fixed
observation cost, as the probability of false alarm goes to zero. We also show,
using simulations, that the two-threshold algorithm has good observation
cost-delay trade-off curves, and provides significant reduction in observation
cost as compared to the naive approach of fractional sampling, where samples
are skipped randomly. Our analysis reveals that, for practical choices of
constraints, the two thresholds can be set independent of each other: one based
on the constraint of false alarm and another based on the observation cost
constraint alone.Comment: Preliminary version of this paper has been presented at ITA Workshop
UCSD 201
Decentralized Sequential Change Detection Using Physical Layer Fusion
Abstract — We study the problem of decentralized sequential change detection with conditionally independent observations. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. Simulations demonstrate that the proposed techniques have lower detection delays when compared with existing schemes. Moreover we demonstrate that the energy-constrained formulation enables better use of the total available energy than a power-constrained formulation. I
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 transmit a simple function of their observations in an analog fashion 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