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Information Flow Optimization in Inference Networks

Citation Author(s):
Jing Liu, Venugopal Veeravalli, Gunjan Verma
Submitted by:
Aditya Deshmukh
Last updated:
13 May 2020 - 5:16pm
Document Type:
Presentation Slides
Document Year:
Event:
Presenters Name:
Aditya Deshmukh
Paper Code:
5173

Abstract 

Abstract: 

The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, compress, and send them to the fusion center through a network of relays. The network imposes capacity constraints on the rate of transmission in each connection and flow conservation constraints. It is shown that this rate-constrained inference problem can be cast as a Network Utility Maximization problem by suitably defining the utility functions for each sensor, and can be solved using existing techniques. Two practical settings are analyzed: multi-terminal parameter estimation and binary hypothesis testing. It is verified via simulations that using the proposed formulation gives better inference performance than the Max-Flow solution that simply maximizes the total bit-rate to the fusion center.

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Poster_Presentation_Information_flow_optimization.pdf

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