Efficient Distributed Sensing Using Adaptive Censoring-Based Inference
Name
2012_VOI_journal.pdf
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909.67 KB
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Adobe PDF
Checksum (MD5)
ed87cd91bcf92c1ff7281a2ebade8afd
Author(s)
Mu, Beipeng
Chowdhary, Girish
How, Jonathan P.
Date Issued
March 15, 2013
Abstract
In many distributed sensing applications it is likely that only a few agents will have valuable information at any given time. Since
wireless communication between agents is resource-intensive, it is important to ensure that the communication effort is focused on
communicating valuable information from informative agents. This paper presents communication efficient distributed sensing algorithms
that avoid network cluttering by having only agents with high Value of Information (VoI) broadcast their measurements to the network,
while others censor themselves. A novel contribution of the presented distributed estimation algorithm is the use of an adaptively adjusted
VoI threshold to determine which agents are informative. This adaptation enables the team to better balance between the communication
cost incurred and the long-term accuracy of the estimation. Theoretical results are presented establishing the almost sure convergence of
the communication cost and estimation error to zero for distributions in the exponential family. Furthermore, validation through numerical
simulations and real datasets show that the new VoI-based algorithms can yield improved parameter estimates than those achieved by
previously published hyperparameter consensus algorithms while incurring only a fraction of the communication cost.
Description
This technical report is a preprint of work submitted to a journal.
Subjects
distributed sensing and inference
value of information
censoring
consensus
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Attribution-NonCommercial-NoDerivs 3.0 United States
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