95,756 research outputs found
Doped Fountain Coding for Minimum Delay Data Collection in Circular Networks
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
Resource location based on precomputed partial random walks in dynamic networks
The problem of finding a resource residing in a network node (the
\emph{resource location problem}) is a challenge in complex networks due to
aspects as network size, unknown network topology, and network dynamics. The
problem is especially difficult if no requirements on the resource placement
strategy or the network structure are to be imposed, assuming of course that
keeping centralized resource information is not feasible or appropriate. Under
these conditions, random algorithms are useful to search the network. A
possible strategy for static networks, proposed in previous work, uses short
random walks precomputed at each network node as partial walks to construct
longer random walks with associated resource information. In this work, we
adapt the previous mechanisms to dynamic networks, where resource instances may
appear in, and disappear from, network nodes, and the nodes themselves may
leave and join the network, resembling realistic scenarios. We analyze the
resulting resource location mechanisms, providing expressions that accurately
predict average search lengths, which are validated using simulation
experiments. Reduction of average search lengths compared to simple random walk
searches are found to be very large, even in the face of high network
volatility. We also study the cost of the mechanisms, focusing on the overhead
implied by the periodic recomputation of partial walks to refresh the
information on resources, concluding that the proposed mechanisms behave
efficiently and robustly in dynamic networks.Comment: 39 pages, 25 figure
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