Decentralized Detection'

By John N. Tsitsiklis. 1993

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Citations in the corpus

Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018, methodology): “Starting with [49], decentralized detection has received attention in the control and signal processing literature, with main focus on information structure, likelihood ratio tests and combining local decisions for global inference.”
Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization (2008, neutral): “08 53”
Defending non-Bayesian learning against adversarial attacks (2016, neutral): “The traditional decentralized detection framework consists of a collection of spatially distributed sensors and a fusion center [5,6,7].”
Optimality guarantees for distributed statistical estimation (2014, background): “In other areas, ranging from theoretical computer science [32, 1, 20], decentralized detection and estimation (e.g., [28, 25]), to information theory (e.g., [17, 15]), there is of course a substantial literature on communication complexity.”
Cooperative Sensing With Imperfect Reporting Channels: Hard Decisions or Soft Decisions? (2012, methodology): “For example, likelihood ratio quantizer (LRQ) [4], [28], locally optimum quantizer [29], and minimum average error (MAE) quantizer [30].”
Data-efficient minimax quickest change detection (2012, background): “Without certain conditional independence assumptions across sensors, the problem of finding the optimal solutions, even in the simplest case of static binary hypothesis testing, is computationally intractable [34]– [39].”
Distributed Detection: Finite-Time Analysis and Impact of Network Topology (2014, background): “2506903 center that decides over the true value of a parameter [1], [2], [8].”
Inference Under Information Constraints III: Local Privacy Constraints (2021, result): “Interestingly, this is in contrast with the seminal work of Tsitsiklis [45], which established that public randomness provides no advantage in the context of distributed simple hypothesis testing without local privacy constraints.”
Toward Information Privacy for the Internet of Things: A Nonparametric Learning Approach (2018, methodology): “This can be modeled using the decentralized detection framework [8]–[14], where each sensor makes an observation, summarizes this observation using a local decision”
Decentralized detection in sensor networks (2003, background): “REFERENCES [1] R. R. Tenney and N. R. Sandell, Jr., “Detection with distributed sensors,” IEEE Trans.”
Distributed Signal Detection under Communication Constraints (2020, background): “From the pioneering works of Tsitsiklis (Tsitsiklis, 1993), and Varshney (Varshney, 2012; Viswanathan and Varshney, 1997), decentralized detection has also received signiﬁcant attention in the control and signal processing literature, with main focus on information structure, likelihood ratio…”
On distributed fault-tolerant detection in wireless sensor networks (2006, background): “The basic idea of distributed detection [7] is to have each of the independent sensors make a local decision (typically, a binary one) and then combine these decisions at a fusion sensor to generate a global decision.”
Cooperative Sensing for Primary Detection in Cognitive Radio (2008, background): “, [5]–[8] for an overview of these results).”
Distributed detection with multiple sensors I. Fundamentals (1997, methodology): “It is shown in [5] that if the likelihood ratio in (5) has no point masses, then an optimal solution within the set of deterministic strategies is also an optimal solution within the set of independent randomization strategies.”
Quantized Consensus (2006, background): “Let S be written as NL+R, where L and R are integers with 0 ≤ R N .”
Universal decentralized estimation in a bandwidth constrained sensor network (2005, background): “Various extensions of these results, especially for large-scale networks, are summarized in the survey paper [19].”
Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013, background): “There is a substantial literature on communication complexity in many settings, including function computation in theoretical computer science (e.g., [21, 1, 13]), decentralized detection and estimation (e.g., [18, 16, 15]) and information theory [11].”