Quantized Network Coding for Sparse Messages

TL;DR

This paper introduces quantized network coding for power grid sensor data, leveraging compressed sensing techniques to improve data compression and delivery efficiency in networks with limited capacity.

Contribution

It proposes a novel quantized network coding method combined with l1-min decoding, utilizing restricted isometry property for robust data recovery.

Findings

Achieves higher compression ratios

Reduces delivery delay

Improves data recovery robustness

Abstract

In this paper, we study the data gathering problem in the context of power grids by using a network of sensors, where the sensed data have inter-node redundancy. Specifically, we propose a new transmission method, calledquantized network coding, which performs linear net-work coding in the field of real numbers, and quantization to accommodate the finite capacity of edges. By using the concepts in compressed sensing literature, we propose to use l1-minimization to decode the quantized network coded packets, especially when the number of received packets at the decoder is less than the size of sensed data (i.e. number of nodes). We also propose an appropriate design for network coding coefficients, based on restricted isometry property, which results in robust l1-min decoding. Our numerical analysis show that the proposed quantized network coding scheme with l1-min decoding can achieve significant improvements, in terms of compression ratio and delivery delay, compared to conventional packet forwarding.