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This paper examines the secrecy in distributed detection under threat of a global eavesdropper (Eve) which has access to all sensors decisions. To measure secrecy, we compare the detection performance at the fusion center (FC) and at Eve in terms of their respective Kullback-Leibler Divergence (KLD). When the channels between sensors and the FC are noiseless and the channels between sensors and Eve...
Distributed detection with dependent observations is always a challenging problem. The problemof detectionwith shared information has many applications when sensors have overlapped measurements, e.g., when distributed detection is performed in a security system where sensors have overlapped coverages. For this shared information scenario, we investigate the distributed detection problem in parallel...
We consider the problem of distributed detection in a wireless network consisting of a large number of sensors having either ideal or non-ideal communication links to their respective fusion or relay node. The detection performance is characterized under a Neyman-Pearson framework using a parallel configuration and a single-rooted tree with bounded height, where only the leaves are sensors. We show...
In this paper, a distributed detection model is introduced for mary hypotheses testing where the local sensors quantize their decisions to messages with alphabet size of D and the number of local sensors is random following a Poisson distribution. This model can be applied to a wide variety of distributed detection problems including homogenous and heterogeneous networks, robust detection under security...
Distributed detection with dependent observations is always a challenging problem. In this paper, we consider a special dependent case where sensors share some common information. Specifically, we investigate a tandem network with sensor 1 sending a one-bit decision to sensor 2 where the final decision is made. Along with their common observation X2, sensors 1 and 2 possess their conditionally independent...
Uniformly Most Powerful (UMP) centralized detection for the composite binary hypothesis problem has been well researched. This paper extends the UMP methodology to the parallel distributed detection problem, when the local observations are independently distributed. A collection of general theorems and corollaries define sufficient conditions for the existence of a UMP parallel Distributed Detection...
In large-scale and dense wireless sensor networks, sensor observations often are correlated and the correlation impacts overall network performance. Another performance limiting factor comes from the non-ideal nature of the wireless links between network nodes. In this paper, we study the detection performance for a distributed detection system with dependent observations under noisy communication...
In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles...
In this paper, a special class of distributed composite binary hypothesis testing problem with monotonic likelihood ratio is investigated. The sensor observations are assumed to be conditionally independent given a fixed but unknown parameter θ where θ ∈ Θ1 under the H1 hypothesis and θ = θ0 under the H0 hypothesis. The optimal form of sensor decision rule is established under both the Neyman-Pearson...
In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles...
Distributed detection with conditionally independent observations at local sensors is well understood. The problem becomes significantly more complicated when dependence is present among sensor observations. In this paper, we attempt to make progress in our understanding of the dependent observation case. Toward this end, we present a new hierarchical model by introducing a hidden or latent variable;...
We consider decentralized detection for resource- constrained wireless sensor networks where local sensor decisions need to go through a multi-hop relay network before reaching the fusion center. Our objective is to collectively design sensor decision rules and relay rules for optimum detection performance. Under the Bayesian criterion, we establish the necessary conditions for an optimal system and...
Stochastic-resonance (SR), a nonlinear physical phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise, has been observed and applied in many areas. However, it has not been shown whether or not this phenomenon plays a role in distributed detection. It seems counterintuitive that adding additional noise to the received decisions at the fusion center...
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