Doped Fountain Coding for Minimum Delay Data Collection in Circular Networks

TL;DR

This paper introduces a decentralized fountain coding strategy with doping for efficient data collection in circular wireless sensor networks, reducing delay by leveraging stochastic data dissemination and message-passing decoding.

Contribution

It proposes a novel doping-based fountain coding approach tailored for circular networks, improving data collection delay and decoding efficiency compared to existing methods.

Findings

Small number of doped packets needed with Ideal Soliton codes

Doping strategy minimizes collection delay at high source density

Circular network topology enhances analytical tractability

Abstract

This paper studies decentralized, Fountain and network-coding based strategies for facilitating data collection in circular wireless sensor networks, which rely on the stochastic diversity of data storage. The goal is to allow for a reduced delay collection by a data collector who accesses the network at a random position and random time. Data dissemination is performed by a set of relays which form a circular route to exchange source packets. The storage nodes within the transmission range of the route's relays linearly combine and store overheard relay transmissions using random decentralized strategies. An intelligent data collector first collects a minimum set of coded packets from a subset of storage nodes in its proximity, which might be sufficient for recovering the original packets and, by using a message-passing decoder, attempts recovering all original source packets from this set. Whenever the decoder stalls, the source packet which restarts decoding is polled/doped from its original source node. The random-walk-based analysis of the decoding/doping process furnishes the collection delay analysis with a prediction on the number of required doped packets. The number of doped packets can be surprisingly small when employed with an Ideal Soliton code degree distribution and, hence, the doping strategy may have the least collection delay when the density of source nodes is sufficiently large. Furthermore, we demonstrate that network coding makes dissemination more efficient at the expense of a larger collection delay. Not surprisingly, a circular network allows for a significantly more (analytically and otherwise) tractable strategies relative to a network whose model is a random geometric graph.