Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimized Sensor Collaboration for Estimation of Temporally Correlated
  Parameters   In this paper, we aim to design the optimal sensor collaboration strategy for
the estimation of time-varying parameters, where collaboration refers to the
act of sharing measurements with neighboring sensors prior to transmission to a
fusion center. We begin by addressing the sensor collaboration problem for the
estimation of uncorrelated parameters. We show that the resulting collaboration
problem can be transformed into a special nonconvex optimization problem, where
a difference of convex functions carries all the nonconvexity. This specific
problem structure enables the use of a convex-concave procedure to obtain a
near-optimal solution. When the parameters of interest are temporally
correlated, a penalized version of the convex-concave procedure becomes well
suited for designing the optimal collaboration scheme. In order to improve
computational efficiency, we further propose a fast algorithm that scales
gracefully with problem size via the alternating direction method of
multipliers. Numerical results are provided to demonstrate the effectiveness of
our approach and the impact of parameter correlation and temporal dynamics of
sensor networks on estimation performance.

5
The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
23
The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: eess