4 research outputs found
Decentralized Sequential Composite Hypothesis Test Based on One-Bit Communication
This paper considers the sequential composite hypothesis test with multiple
sensors. The sensors observe random samples in parallel and communicate with a
fusion center, who makes the global decision based on the sensor inputs. On one
hand, in the centralized scenario, where local samples are precisely
transmitted to the fusion center, the generalized sequential likelihood ratio
test (GSPRT) is shown to be asymptotically optimal in terms of the expected
sample size as error rates tend to zero. On the other hand, for systems with
limited power and bandwidth resources, decentralized solutions that only send a
summary of local samples (we particularly focus on a one-bit communication
protocol) to the fusion center is of great importance. To this end, we first
consider a decentralized scheme where sensors send their one-bit quantized
statistics every fixed period of time to the fusion center. We show that such a
uniform sampling and quantization scheme is strictly suboptimal and its
suboptimality can be quantified by the KL divergence of the distributions of
the quantized statistics under both hypotheses. We then propose a decentralized
GSPRT based on level-triggered sampling. That is, each sensor runs its own
GSPRT repeatedly and reports its local decision to the fusion center
asynchronously. We show that this scheme is asymptotically optimal as the local
thresholds and global thresholds grow large at different rates. Lastly, two
particular models and their associated applications are studied to compare the
centralized and decentralized approaches. Numerical results are provided to
demonstrate that the proposed level-triggered sampling based decentralized
scheme aligns closely with the centralized scheme with substantially lower
communication overhead, and significantly outperforms the uniform sampling and
quantization based decentralized scheme.Comment: 39 page
Sequential Hypothesis Test with Online Usage-Constrained Sensor Selection
This work investigates the sequential hypothesis testing problem with online
sensor selection and sensor usage constraints. That is, in a sensor network,
the fusion center sequentially acquires samples by selecting one "most
informative" sensor at each time until a reliable decision can be made. In
particular, the sensor selection is carried out in the online fashion since it
depends on all the previous samples at each time. Our goal is to develop the
sequential test (i.e., stopping rule and decision function) and sensor
selection strategy that minimize the expected sample size subject to the
constraints on the error probabilities and sensor usages. To this end, we first
recast the usage-constrained formulation into a Bayesian optimal stopping
problem with different sampling costs for the usage-contrained sensors. The
Bayesian problem is then studied under both finite- and infinite-horizon
setups, based on which, the optimal solution to the original usage-constrained
problem can be readily established. Moreover, by capitalizing on the structures
of the optimal solution, a lower bound is obtained for the optimal expected
sample size. In addition, we also propose algorithms to approximately evaluate
the parameters in the optimal sequential test so that the sensor usage and
error probability constraints are satisfied. Finally, numerical experiments are
provided to illustrate the theoretical findings, and compare with the existing
methods.Comment: 33 page
Order-2 Asymptotic Optimality of the Fully Distributed Sequential Hypothesis Test
This work analyzes the asymptotic performances of fully distributed
sequential hypothesis testing procedures as the type-I and type-II error rates
approach zero, in the context of a sensor network without a fusion center. In
particular, the sensor network is defined by an undirected graph, where each
sensor can observe samples over time, access the information from the adjacent
sensors, and perform the sequential test based on its own decision statistic.
Different from most literature, the sampling process and the information
exchange process in our framework take place simultaneously (or, at least in
comparable time-scales), thus cannot be decoupled from one another. Two
message-passing schemes are considered, based on which the distributed
sequential probability ratio test (DSPRT) is carried out respectively. The
first scheme features the dissemination of the raw samples. Although the sample
propagation based DSPRT is shown to yield the asymptotically optimal
performance at each sensor, it incurs excessive inter-sensor communication
overhead due to the exchange of raw samples with index information. The second
scheme adopts the consensus algorithm, where the local decision statistic is
exchanged between sensors instead of the raw samples, thus significantly
lowering the communication requirement compared to the first scheme. In
particular, the decision statistic for DSPRT at each sensor is updated by the
weighted average of the decision statistics in the neighbourhood at every
message-passing step. We show that, under certain regularity conditions, the
consensus algorithm based DSPRT also yields the order-2 asymptotically optimal
performance at all sensors.Comment: 36 page
Asymptotically Optimal Stochastic Encryption for Quantized Sequential Detection in the Presence of Eavesdroppers
We consider sequential detection based on quantized data in the presence of
eavesdropper. Stochastic encryption is employed as a counter measure that flips
the quantization bits at each sensor according to certain probabilities, and
the flipping probabilities are only known to the legitimate fusion center (LFC)
but not the eavesdropping fusion center (EFC). As a result, the LFC employs the
optimal sequential probability ratio test (SPRT) for sequential detection
whereas the EFC employs a mismatched SPRT (MSPRT). We characterize the
asymptotic performance of the MSPRT in terms of the expected sample size as a
function of the vanishing error probabilities. We show that when the detection
error probabilities are set to be the same at the LFC and EFC, every symmetric
stochastic encryption is ineffective in the sense that it leads to the same
expected sample size at the LFC and EFC. Next, in the asymptotic regime of
small detection error probabilities, we show that every stochastic encryption
degrades the performance of the quantized sequential detection at the LFC by
increasing the expected sample size, and the expected sample size required at
the EFC is no fewer than that is required at the LFC. Then the optimal
stochastic encryption is investigated in the sense of maximizing the difference
between the expected sample sizes required at the EFC and LFC. Although this
optimization problem is nonconvex, we show that if the acceptable tolerance of
the increase in the expected sample size at the LFC induced by the stochastic
encryption is small enough, then the globally optimal stochastic encryption can
be analytically obtained; and moreover, the optimal scheme only flips one type
of quantized bits (i.e., 1 or 0) and keeps the other type unchanged