Information Flow Optimization in Inference Networks

Aditya Deshmukh; Jing Liu; Venugopal V. Veeravalli; Gunjan Verma

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arXiv · 1910.11451

Information Flow Optimization in Inference Networks

Aditya Deshmukh·Jing Liu·Venugopal V. Veeravalli·Gunjan Verma

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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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BibTeX RIS

Aditya Deshmukh, Jing Liu, Venugopal V. Veeravalli, Gunjan Verma. 2019-10-24. Information Flow Optimization in Inference Networks. https://arxiv.org/abs/1910.11451

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