Efficient distributed sensing using adaptive censoring-based inference
Name
2012_VOI_journal.pdf
Description
submitted to Automatica
Size
909.67 KB
Format
Adobe PDF
Checksum (MD5)
ed87cd91bcf92c1ff7281a2ebade8afd
Author(s)
Mu, Beipeng
Chowdhary, Girish
How, Jonathan P
Date Issued
April 2014
Journal
Automatica
Publisher
Elsevier
Citation
Mu, Beipeng, Girish Chowdhary, and Jonathan P. How. “Efficient Distributed Sensing Using Adaptive Censoring-Based Inference.” Automatica 50, no. 6 (June 2014): 1590–1602.
Version
Author's final manuscript
Abstract
In many distributed sensing applications with limited resources, it is likely that only a few agents will have valuable information at any given time. Therefore it is important to ensure that the resources are spent 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 for distributions in the exponential family. Furthermore, validation through numerical simulations and real datasets shows 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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.automatica.2014.04.013
