71 research outputs found
Infocast: A New Paradigm for Collaborative Content Distribution from Roadside Units to Vehicular Networks Using Rateless Codes
In this paper, we address the problem of distributing a large amount of bulk
data to a sparse vehicular network from roadside infostations, using efficient
vehicle-to-vehicle collaboration. Due to the highly dynamic nature of the
underlying vehicular network topology, we depart from architectures requiring
centralized coordination, reliable MAC scheduling, or global network state
knowledge, and instead adopt a distributed paradigm with simple protocols. In
other words, we investigate the problem of reliable dissemination from multiple
sources when each node in the network shares a limited amount of its resources
for cooperating with others. By using \emph{rateless} coding at the Road Side
Unit (RSU) and using vehicles as data carriers, we describe an efficient way to
achieve reliable dissemination to all nodes (even disconnected clusters in the
network). In the nutshell, we explore vehicles as mobile storage devices. We
then develop a method to keep the density of the rateless codes packets as a
function of distance from the RSU at the desired level set for the target
decoding distance. We investigate various tradeoffs involving buffer size,
maximum capacity, and the mobility parameter of the vehicles
Results on the Redundancy of Universal Compression for Finite-Length Sequences
In this paper, we investigate the redundancy of universal coding schemes on
smooth parametric sources in the finite-length regime. We derive an upper bound
on the probability of the event that a sequence of length , chosen using
Jeffreys' prior from the family of parametric sources with unknown
parameters, is compressed with a redundancy smaller than
for any . Our results also confirm
that for large enough and , the average minimax redundancy provides a
good estimate for the redundancy of most sources. Our result may be used to
evaluate the performance of universal source coding schemes on finite-length
sequences. Additionally, we precisely characterize the minimax redundancy for
two--stage codes. We demonstrate that the two--stage assumption incurs a
negligible redundancy especially when the number of source parameters is large.
Finally, we show that the redundancy is significant in the compression of small
sequences.Comment: accepted in the 2011 IEEE International Symposium on Information
Theory (ISIT 2011
BPRS: Belief Propagation Based Iterative Recommender System
In this paper we introduce the first application of the Belief Propagation
(BP) algorithm in the design of recommender systems. We formulate the
recommendation problem as an inference problem and aim to compute the marginal
probability distributions of the variables which represent the ratings to be
predicted. However, computing these marginal probability functions is
computationally prohibitive for large-scale systems. Therefore, we utilize the
BP algorithm to efficiently compute these functions. Recommendations for each
active user are then iteratively computed by probabilistic message passing. As
opposed to the previous recommender algorithms, BPRS does not require solving
the recommendation problem for all the users if it wishes to update the
recommendations for only a single active. Further, BPRS computes the
recommendations for each user with linear complexity and without requiring a
training period. Via computer simulations (using the 100K MovieLens dataset),
we verify that BPRS iteratively reduces the error in the predicted ratings of
the users until it converges. Finally, we confirm that BPRS is comparable to
the state of art methods such as Correlation-based neighborhood model (CorNgbr)
and Singular Value Decomposition (SVD) in terms of rating and precision
accuracy. Therefore, we believe that the BP-based recommendation algorithm is a
new promising approach which offers a significant advantage on scalability
while providing competitive accuracy for the recommender systems
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