Distributed Consensus With Quantized Data via Sequence Averaging

The problem of distributed average consensus with quantized data is considered in this correspondence. Conventional consensus algorithms suffer from divergence when quantization errors are present. To address this issue, we introduce a modified quantization-based consensus protocol and exploit the t...

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Vydané v:IEEE transactions on signal processing Ročník 58; číslo 2; s. 944 - 948
Hlavní autori: Fang, Jun, Li, Hongbin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.02.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Acceleration
Algorithms
Applied sciences
Communication system control
Convergence
Detection, estimation, filtering, equalization, prediction
Distributed average consensus
Exact sciences and technology
Information, signal and communications theory
INT
Iterative algorithms
Mean square values
Miscellaneous
Networks
Nonlinearity
Parameter estimation
Protocols
Quantization
Random variables
Robust control
Sampling, quantization
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Temporal logic
wireless sensor network (WSN)
Wireless sensor networks
Performance evaluation
Averaging method
Quantization error
Wireless telecommunication
Iterative method
Algorithm
Remote sensing
Robust control
Algorithm performance
Signal quantization
Signal processing
wireless sensor network (WSN)
Wireless network
Numerical simulation
Sensor array
Signal detection
Distributed average consensus
quantization
ISSN:1053-587X, 1941-0476
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Popis
Shrnutí:The problem of distributed average consensus with quantized data is considered in this correspondence. Conventional consensus algorithms suffer from divergence when quantization errors are present. To address this issue, we introduce a modified quantization-based consensus protocol and exploit the temporal information collected from the iterative process, based on which we develop an efficient consensus algorithm. The proposed consensus algorithm is proved to converge to the true mean, i.e., the average of the initial state, in a mean square sense. It also presents an advantage of speeding up the convergence over the algorithm [P. Frasca, R. Carli, F. Fagnani, and S. Zampieri, ¿Average Consensus on Networks With Quantized Communication,¿ Int. J. Robust Non-Linear Control, 2008, to be published] without exploitation of temporal information. Numerical results are presented to illustrate the effectiveness of the proposed algorithm.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2009.2032951