15,305 research outputs found
Theoretical Bounds in Minimax Decentralized Hypothesis Testing
Minimax decentralized detection is studied under two scenarios: with and
without a fusion center when the source of uncertainty is the Bayesian prior.
When there is no fusion center, the constraints in the network design are
determined. Both for a single decision maker and multiple decision makers, the
maximum loss in detection performance due to minimax decision making is
obtained. In the presence of a fusion center, the maximum loss of detection
performance between with- and without fusion center networks is derived
assuming that both networks are minimax robust. The results are finally
generalized.Comment: Submitted to IEEE Trans. on Signal Processin
Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks
Sensor networks aim at monitoring their surroundings for event detection and
object tracking. But, due to failure, or death of sensors, false signal can be
transmitted. In this paper, we consider the problems of distributed fault
detection in wireless sensor network (WSN). In particular, we consider how to
take decision regarding fault detection in a noisy environment as a result of
false detection or false response of event by some sensors, where the sensors
are placed at the center of regular hexagons and the event can occur at only
one hexagon. We propose fault detection schemes that explicitly introduce the
error probabilities into the optimal event detection process. We introduce two
types of detection probabilities, one for the center node, where the event
occurs and the other one for the adjacent nodes. This second type of detection
probability is new in sensor network literature. We develop schemes under the
model selection procedure, multiple model selection procedure and use the
concept of Bayesian model averaging to identify a set of likely fault sensors
and obtain an average predictive error.Comment: 14 page
Distributed Detection in Sensor Networks with Limited Range Sensors
We consider a multi-object detection problem over a sensor network (SNET)
with limited range sensors. This problem complements the widely considered
decentralized detection problem where all sensors observe the same object.
While the necessity for global collaboration is clear in the decentralized
detection problem, the benefits of collaboration with limited range sensors is
unclear and has not been widely explored. In this paper we develop a
distributed detection approach based on recent development of the false
discovery rate (FDR). We first extend the FDR procedure and develop a
transformation that exploits complete or partial knowledge of either the
observed distributions at each sensor or the ensemble (mixture) distribution
across all sensors. We then show that this transformation applies to
multi-dimensional observations, thus extending FDR to multi-dimensional
settings. We also extend FDR theory to cases where distributions under both
null and positive hypotheses are uncertain. We then propose a robust
distributed algorithm to perform detection. We further demonstrate scalability
to large SNETs by showing that the upper bound on the communication complexity
scales linearly with the number of sensors that are in the vicinity of objects
and is independent of the total number of sensors. Finally, we deal with
situations where the sensing model may be uncertain and establish robustness of
our techniques to such uncertainties.Comment: Submitted to IEEE Transactions on Signal Processin
Submodularity and Optimality of Fusion Rules in Balanced Binary Relay Trees
We study the distributed detection problem in a balanced binary relay tree,
where the leaves of the tree are sensors generating binary messages. The root
of the tree is a fusion center that makes the overall decision. Every other
node in the tree is a fusion node that fuses two binary messages from its child
nodes into a new binary message and sends it to the parent node at the next
level. We assume that the fusion nodes at the same level use the same fusion
rule. We call a string of fusion rules used at different levels a fusion
strategy. We consider the problem of finding a fusion strategy that maximizes
the reduction in the total error probability between the sensors and the fusion
center. We formulate this problem as a deterministic dynamic program and
express the solution in terms of Bellman's equations. We introduce the notion
of stringsubmodularity and show that the reduction in the total error
probability is a stringsubmodular function. Consequentially, we show that the
greedy strategy, which only maximizes the level-wise reduction in the total
error probability, is within a factor of the optimal strategy in terms of
reduction in the total error probability
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