scholarly journals Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 167865-167874
Author(s):  
Jinran Wang ◽  
Peng Dong ◽  
Kai Shen ◽  
Xun Song ◽  
Xiaodong Wang
Keyword(s):  
Sensor Networks ◽  
Distributed Consensus ◽  
Measurement Noises ◽  
Heavy Tailed

scholarly journals Self-Aware Distributed Consensus Building for Sensor Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
Lei Chen ◽  
Jeff Frolik
Keyword(s):  
Asymptotic Behavior ◽  
Sensor Networks ◽  
Extensive Study ◽  
Practical Implementation ◽  
Consensus Building ◽  
Optimal Parameters ◽  
Distributed Consensus ◽  
Systematic Analysis ◽  
Second Derivatives ◽  
Derivatives Of

Distributed consensus building promises to improve the robustness and reliability of sensor networks and thus is an active topic of research. Whereas extensive study has been done on the theoretical analysis of the asymptotic behavior of consensus building, one important issue that is crucial to the practical implementation of sensor networks was rarely explored, namely, the criteria to determine whether consensus has been attained. In this paper, we propose an approach that allows each node in a network to make the decision by itself, based on the second derivatives of its own state. The approach does not rely on the states of other nodes, leads to substantial saving of communication resources, and is resilient to connection failure. We perform a systematic analysis of the approach and, as a consequence, derive the optimal parameters that minimize the upper bound of the number of required iterations to reach consensus.

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