  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.
