A Context-Aware CEO Problem
Daewon Seo, Sung Hoon Lim, Yongjune Kim

TL;DR
This paper extends the CEO problem in sensor networks by incorporating context information, characterizing optimal error exponents, and proposing efficient sensor grouping strategies, with numerical validation in Gaussian scenarios.
Contribution
It introduces a generalized CEO problem with context data, deriving asymptotic error exponents and optimal sensor grouping schemes, extending previous models without context.
Findings
Derived the asymptotic error exponent per rate with context information.
Proved optimal sensor grouping into at most rac{|\u00Xx|rac{2}{}}{2} |S| groups.
Numerically demonstrated the impact of context in Gaussian scenarios.
Abstract
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 and , we prove that it is asymptotically optimal to partition all sensors into at most…
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms
