Detection networks

Ekchian, Leon K.; Tenney, Robert R.

doi:10.1109/cdc.1982.268228

Cited by 57 publications

(32 citation statements)

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“…Special cases of the tandem configuration (sensors arranged in a serial network), and some specific tree configurations have been studied in [17], where it is shown that it is optimal for sensor nodes to employ likelihood ratio quantizers. Tree configurations are also discussed in [6,[18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Error Exponents for Decentralized Detection in Tree Networks

Tay

Tsitsiklis

2008

Networked Sensing Information and Control

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Summary. We consider the problem of decentralized detection in a sensor network consisting of nodes arranged as a tree of bounded height. We characterize the optimal error exponent under a Neyman-Pearson formulation, and show that the Type II error probability decays exponentially with the number of nodes. Surprisingly, the optimal error exponent is often the same as that corresponding to a parallel configuration. We provide sufficient, as well as necessary, conditions for this to happen. We also consider the impact of failure-prone sensors or unreliable communications between sensors on the detection performance. Simple strategies that nearly achieve the asymptotically optimal performance in these cases are also developed.

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Section: Introductionmentioning

confidence: 99%

Error Exponents for Decentralized Detection in Tree Networks

Tay

Tsitsiklis

2008

Networked Sensing Information and Control

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“…It is not hard to verify that with this construction and under our assumption that the distributions F 0 and F 1 are not identical, the distributions of s n under the two states of the world will be different. 8 We also note that it suffices to construct a decision profile for the case where K = 2. Indeed, if K > 2, we can have the agents ignore the actions of all but their two immediate predecessors and employ the decision profile designed for the case where K = 2.…”

Section: A Reduction To the Case Of Binary Observationsmentioning

confidence: 99%

“…3 The problem is the design of decision rules for each agent so as to minimize the probability of error at the fusion center. A more general network structure, in which each agent observes messages from a specific set of agents before making a decision was introduced in [7] and [8], under the assumption that the topology that describes the message flow is a directed tree. In all of this literature (and under the assumption that the private signals are conditionally independent, given the true hypothesis) each agent's decision rule should be a likelihood ratio test, parameterized by a scalar threshold.…”

Section: A Statistics/engineering Literaturementioning

confidence: 99%

On Learning With Finite Memory

Drakopoulos¹,

Ozdaglar²,

Tsitsiklis³

2013

IEEE Trans. Inform. Theory

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Abstract-We consider an infinite collection of agents who make decisions, sequentially, about an unknown underlying binary state of the world. Each agent, prior to making a decision, receives an independent private signal whose distribution depends on the state of the world. Moreover, each agent also observes the decisions of its last K immediate predecessors. We study conditions under which the agent decisions converge to the correct value of the underlying state.We focus on the case where the private signals have bounded information content and investigate whether learning is possible, that is, whether there exist decision rules for the different agents that result in the convergence of their sequence of individual decisions to the correct state of the world. We first consider learning in the almost sure sense and show that it is impossible, for any value of K. We then explore the possibility of convergence in probability of the decisions to the correct state. Here, a distinction arises: if K = 1, learning in probability is impossible under any decision rule, while for K ≥ 2, we design a decision rule that achieves it.We finally consider a new model, involving forward looking strategic agents, each of which maximizes the discounted sum (over all agents) of the probabilities of a correct decision. (The case, studied in previous literature, of myopic agents who maximize the probability of their own decision being correct is an extreme special case.) We show that for any value of K, for any equilibrium of the associated Bayesian game, and under the assumption that each private signal has bounded information content, learning in probability fails to obtain.

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“…Ekchian and Tenney [22] derived the necessary conditions for the thresholds to satisfy in order to minimize a given cost function for a number of configurations including the tandem and the tree-hierarchical topologies. No numerical results were provided.…”

Section: Decentralized Detection Systemsmentioning

confidence: 99%

ALGORITHMS FOR SENSOR FUSION: Applications of Distance Measures and Probability of Error Bounds to Distributed. Detection Systems. Volume 2

Varshney¹,

Hashlamoun²

1991

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Detection networks

Cited by 57 publications

References 0 publications

Error Exponents for Decentralized Detection in Tree Networks

Error Exponents for Decentralized Detection in Tree Networks

On Learning With Finite Memory

ALGORITHMS FOR SENSOR FUSION: Applications of Distance Measures and Probability of Error Bounds to Distributed. Detection Systems. Volume 2

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