3,718 research outputs found
Randomized Consensus with Attractive and Repulsive Links
We study convergence properties of a randomized consensus algorithm over a
graph with both attractive and repulsive links. At each time instant, a node is
randomly selected to interact with a random neighbor. Depending on if the link
between the two nodes belongs to a given subgraph of attractive or repulsive
links, the node update follows a standard attractive weighted average or a
repulsive weighted average, respectively. The repulsive update has the opposite
sign of the standard consensus update. In this way, it counteracts the
consensus formation and can be seen as a model of link faults or malicious
attacks in a communication network, or the impact of trust and antagonism in a
social network. Various probabilistic convergence and divergence conditions are
established. A threshold condition for the strength of the repulsive action is
given for convergence in expectation: when the repulsive weight crosses this
threshold value, the algorithm transits from convergence to divergence. An
explicit value of the threshold is derived for classes of attractive and
repulsive graphs. The results show that a single repulsive link can sometimes
drastically change the behavior of the consensus algorithm. They also
explicitly show how the robustness of the consensus algorithm depends on the
size and other properties of the graphs
Space-Time Tradeoffs for Distributed Verification
Verifying that a network configuration satisfies a given boolean predicate is
a fundamental problem in distributed computing. Many variations of this problem
have been studied, for example, in the context of proof labeling schemes (PLS),
locally checkable proofs (LCP), and non-deterministic local decision (NLD). In
all of these contexts, verification time is assumed to be constant. Korman,
Kutten and Masuzawa [PODC 2011] presented a proof-labeling scheme for MST, with
poly-logarithmic verification time, and logarithmic memory at each vertex.
In this paper we introduce the notion of a -PLS, which allows the
verification procedure to run for super-constant time. Our work analyzes the
tradeoffs of -PLS between time, label size, message length, and computation
space. We construct a universal -PLS and prove that it uses the same amount
of total communication as a known one-round universal PLS, and factor
smaller labels. In addition, we provide a general technique to prove lower
bounds for space-time tradeoffs of -PLS. We use this technique to show an
optimal tradeoff for testing that a network is acyclic (cycle free). Our
optimal -PLS for acyclicity uses label size and computation space . We further describe a recursive space verifier for
acyclicity which does not assume previous knowledge of the run-time .Comment: Pre-proceedings version of paper presented at the 24th International
Colloquium on Structural Information and Communication Complexity (SIROCCO
2017
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
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