5 research outputs found
Advances in Distributed Graph Filtering
Graph filters are one of the core tools in graph signal processing. A central
aspect of them is their direct distributed implementation. However, the
filtering performance is often traded with distributed communication and
computational savings. To improve this tradeoff, this work generalizes
state-of-the-art distributed graph filters to filters where every node weights
the signal of its neighbors with different values while keeping the aggregation
operation linear. This new implementation, labeled as edge-variant graph
filter, yields a significant reduction in terms of communication rounds while
preserving the approximation accuracy. In addition, we characterize the subset
of shift-invariant graph filters that can be described with edge-variant
recursions. By using a low-dimensional parametrization the proposed graph
filters provide insights in approximating linear operators through the
succession and composition of local operators, i.e., fixed support matrices,
which span applications beyond the field of graph signal processing. A set of
numerical results shows the benefits of the edge-variant filters over current
methods and illustrates their potential to a wider range of applications than
graph filtering
DISTRIBUTED SIGNAL SUBSPACE PROJECTION ALGORITHMS WITH MAXIMUM CONVERGENCE RATE FOR SENSOR NETWORKS WITH TOPOLOGICAL CONSTRAINTS
The observations gathered by the individual nodes of a sensor network may be unreliable due to malfunctioning, observation noise or low battery level. Global reliability is typically recovered by collecting all the measurements in a fusion center which takes proper decisions. However, centralized networks are more vulnerable and prone to congestion around the sink nodes. To relax the congestion problem, decrease the network vulnerability and improve the network efficiency, it is appropriate to bring the decisions at the lowest possible level. In this paper, we propose a distributed algorithm allowing each node to improve the reliability of its own reading thanks to the interaction with the other nodes, assuming that the field monitored by the network is a smooth function. In mathematical terms, this only requires that the useful field belongs to a subspace of dimension smaller than the number of nodes. Although fully decentralized, the proposed algorithm is globally optimal, in the sense that it performs the projection of the overall set of observations onto the signal subspace through an iterative decentralized algorithms, that requires minimum convergence time, for any given node coverage. ©2009 IEEE
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