Universal decentralized estimation in a bandwidth constrained sensor network

By Zhi-Quan Tom Luo. IEEE Transactions on Information Theory, 2005

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f85c47d9487462566c58f8862b6b81af9dd90a2b

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Citations in the corpus

Bandwidth-constrained distributed estimation for wireless sensor networks-part II: unknown probability density function (2006, background): “Luo of the University of Minnesota for his valuable feedback and for generously providing preprints [12]–[14].”
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case (2006, background): “Alternatively, when the noise pdf is unknown, universal (pdf-unaware) estimators based on quantized sensor data have been introduced recently [11]–[13].”
Power scheduling of universal decentralized estimation in sensor networks (2006, methodology): “The sensors collect real-valued data, perform a local data compression and send the resulting discrete messages to the fusion center, while the latter combines the received messages to produce a final estimate of the observed signal [11].”
Distributed compression-estimation using wireless sensor networks (2006, neutral): “© P H O T O C R E D IT © IM A G E S TA T E [The design goals of performance, bandwidth efficiency, scalability, and robustness]”
Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013, background): “For instance, Luo [15] considers architectures in which machines may send only a single bit to a centralized processor; for certain problems, he shows that if each machine receives a single one-dimensional sample, it is possible to achieve the optimal centralized rate up to constant factors.”
Estimation Diversity and Energy Efficiency in Distributed Sensing (2007, background): “In [17], the author considers the universal distributed estimation in a homogeneous sens or network where sensors have observations of the same quality , while in [18], the universal DES in an inhomogeneous sensing environment is considered.”
Optimality guarantees for distributed statistical estimation (2014, background): “Study of communication complexity has also given rise to interesting algorithmic schemes; for example, Luo [23] considers architectures in which machines may send only a single bit to a centralized processor; for certain problems, he shows that if each machine receives a single one-dimensional…”
Dealing with Range Anxiety in Mean Estimation via Statistical Queries (2016, background): “This step is likely to be slow or even impossible in some systems (such as the sensor systems considered in [Luo05]).”
Minimax Quantization for Distributed Maximum Likelihood Estimation (2006, methodology): “For a deterministic parameter, Luo[5] optimized the number of quantization bits for a uniform quantizer to minimize MSE.”