27,776 research outputs found
Online Distributed Sensor Selection
A key problem in sensor networks is to decide which sensors to query when, in
order to obtain the most useful information (e.g., for performing accurate
prediction), subject to constraints (e.g., on power and bandwidth). In many
applications the utility function is not known a priori, must be learned from
data, and can even change over time. Furthermore for large sensor networks
solving a centralized optimization problem to select sensors is not feasible,
and thus we seek a fully distributed solution. In this paper, we present
Distributed Online Greedy (DOG), an efficient, distributed algorithm for
repeatedly selecting sensors online, only receiving feedback about the utility
of the selected sensors. We prove very strong theoretical no-regret guarantees
that apply whenever the (unknown) utility function satisfies a natural
diminishing returns property called submodularity. Our algorithm has extremely
low communication requirements, and scales well to large sensor deployments. We
extend DOG to allow observation-dependent sensor selection. We empirically
demonstrate the effectiveness of our algorithm on several real-world sensing
tasks
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In this
paper, we study its associated optimization problem in the distributed setting
where the elements to be combined are not centrally located but spread over a
network. We address the key challenges of balancing communication costs and
optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW)
algorithm. We obtain theoretical guarantees on the optimization error
and communication cost that do not depend on the total number of
combining elements. We further show that the communication cost of dFW is
optimal by deriving a lower-bound on the communication cost required to
construct an -approximate solution. We validate our theoretical
analysis with empirical studies on synthetic and real-world data, which
demonstrate that dFW outperforms both baselines and competing methods. We also
study the performance of dFW when the conditions of our analysis are relaxed,
and show that dFW is fairly robust.Comment: Extended version of the SIAM Data Mining 2015 pape
Capacity of 1-to-K Broadcast Packet Erasure Channels with Channel Output Feedback
This paper focuses on the 1-to-K broadcast packet erasure channel (PEC),
which is a generalization of the broadcast binary erasure channel from the
binary symbol to that of arbitrary finite fields GF(q) with sufficiently large
q. We consider the setting in which the source node has instant feedback of the
channel outputs of the K receivers after each transmission. Such a setting
directly models network coded packet transmission in the downlink direction
with integrated feedback mechanisms (such as Automatic Repeat reQuest (ARQ)).
The main results of this paper are: (i) The capacity region for general
1-to-3 broadcast PECs, and (ii) The capacity region for two classes of 1-to-K
broadcast PECs: the symmetric PECs, and the spatially independent PECs with
one-sided fairness constraints. This paper also develops (iii) A pair of outer
and inner bounds of the capacity region for arbitrary 1-to-K broadcast PECs,
which can be evaluated by any linear programming solver. For most practical
scenarios, the outer and inner bounds meet and thus jointly characterize the
capacity.Comment: 8 pages, 2 figures. Published in Allerton 2010. The journal version
of this work was submitted to IEEE Trans IT in May, 201
Wireless Broadcast with Network Coding in Mobile Ad-Hoc Networks: DRAGONCAST
Network coding is a recently proposed method for transmitting data, which has
been shown to have potential to improve wireless network performance. We study
network coding for one specific case of multicast, broadcasting, from one
source to all nodes of the network. We use network coding as a loss tolerant,
energy-efficient, method for broadcast. Our emphasis is on mobile networks. Our
contribution is the proposal of DRAGONCAST, a protocol to perform network
coding in such a dynamically evolving environment. It is based on three
building blocks: a method to permit real-time decoding of network coding, a
method to adjust the network coding transmission rates, and a method for
ensuring the termination of the broadcast. The performance and behavior of the
method are explored experimentally by simulations; they illustrate the
excellent performance of the protocol
Near Optimal Parallel Algorithms for Dynamic DFS in Undirected Graphs
Depth first search (DFS) tree is a fundamental data structure for solving
graph problems. The classical algorithm [SiComp74] for building a DFS tree
requires time for a given graph having vertices and edges.
Recently, Baswana et al. [SODA16] presented a simple algorithm for updating DFS
tree of an undirected graph after an edge/vertex update in time.
However, their algorithm is strictly sequential. We present an algorithm
achieving similar bounds, that can be adopted easily to the parallel
environment.
In the parallel model, a DFS tree can be computed from scratch using
processors in expected time [SiComp90] on an EREW PRAM, whereas
the best deterministic algorithm takes time
[SiComp90,JAlg93] on a CRCW PRAM. Our algorithm can be used to develop optimal
(upto polylog n factors deterministic algorithms for maintaining fully dynamic
DFS and fault tolerant DFS, of an undirected graph.
1- Parallel Fully Dynamic DFS:
Given an arbitrary online sequence of vertex/edge updates, we can maintain a
DFS tree of an undirected graph in time per update using
processors on an EREW PRAM.
2- Parallel Fault tolerant DFS:
An undirected graph can be preprocessed to build a data structure of size
O(m) such that for a set of updates (where is constant) in the graph,
the updated DFS tree can be computed in time using
processors on an EREW PRAM.
Moreover, our fully dynamic DFS algorithm provides, in a seamless manner,
nearly optimal (upto polylog n factors) algorithms for maintaining a DFS tree
in semi-streaming model and a restricted distributed model. These are the first
parallel, semi-streaming and distributed algorithms for maintaining a DFS tree
in the dynamic setting.Comment: Accepted to appear in SPAA'17, 32 Pages, 5 Figure
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