A Context-Aware CEO Problem

In many sensor network applications, a fusion center often has additional valuable information, such as context data, which cannot be obtained directly from the sensors. Motivated by this, we study a generalized CEO problem where a CEO has access to context information. The main contribution of this...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 71; no. 12; pp. 6979 - 6992
Main Authors: Seo, Daewon, Lim, Sung Hoon, Kim, Yongjune
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Asymptotic properties
CEO problem
Context
distributed source coding
error exponent
Error probability
Internet of Things
Noise measurement
Programming
Sensors
side information
Source coding
Task analysis
Testing
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:In many sensor network applications, a fusion center often has additional valuable information, such as context data, which cannot be obtained directly from the sensors. Motivated by this, we study a generalized CEO problem where a CEO has access to context information. The main contribution of this work is twofold. Firstly, we characterize the asymptotically optimal error exponent per rate as the number of sensors and sum rate grow without bound. The proof extends the Berger-Tung coding scheme and the converse argument by Berger et al. (1996) taking into account context information. The resulting expression includes the minimum Chernoff divergence over context information. Secondly, assuming that the sizes of the source and context alphabets are respectively <inline-formula> <tex-math notation="LaTeX">|\mathcal {X}| </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">|\mathcal {S}| </tex-math></inline-formula>, we prove that it is asymptotically optimal to partition all sensors into at most <inline-formula> <tex-math notation="LaTeX">\binom {|\mathcal {X}|}{2} |\mathcal {S}| </tex-math></inline-formula> groups and have the sensors in each group adopt the same encoding scheme. Our problem subsumes the original CEO problem by Berger et al. (1996) as a special case if there is only one letter for context information; in this case, our result tightens its required number of groups from <inline-formula> <tex-math notation="LaTeX">\binom {|\mathcal {X}|}{2}+2 </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">\binom {|\mathcal {X}|}{2} </tex-math></inline-formula>. We also numerically demonstrate the effect of context information for a simple Gaussian scenario.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2023.3317916