Energy Efficient Distributed Coding for Data Collection in a Noisy Sparse Network

TL;DR

This paper introduces an energy-efficient distributed coding scheme for data collection in noisy, sparse networks, significantly reducing energy use and approaching theoretical optimality by extending coding techniques to sparse graph structures.

Contribution

It proposes a novel in-network coding scheme for sparse networks that reduces energy consumption by a logarithmic factor and establishes near-optimal bounds, extending Gallager's noisy broadcasting work.

Findings

Energy consumption reduced by a factor of Θ(log N)

Scheme is nearly energy-optimal within a Θ(log log N) factor

Extends coding techniques to sparse network topologies

Abstract

We consider the problem of data collection in a two-layer network consisting of (1) links between $N$ distributed agents and a remote sink node; (2) a sparse network formed by these distributed agents. We study the effect of inter-agent communications on the overall energy consumption. Despite the sparse connections between agents, we provide an in-network coding scheme that reduces the overall energy consumption by a factor of $\Theta(\log N)$ compared to a naive scheme which neglects inter-agent communications. By providing lower bounds on both the energy consumption and the sparseness (number of links) of the network, we show that are energy-optimal except for a factor of $\Theta(\log\log N)$. The proposed scheme extends a previous work of Gallager on noisy broadcasting from a complete graph to a sparse graph, while bringing in new techniques from error control coding and noisy circuits.