613 research outputs found
Distributed estimation and control of node centrality in undirected asymmetric networks
Measures of node centrality that describe the importance of a node within a
network are crucial for understanding the behavior of social networks and
graphs. In this paper, we address the problems of distributed estimation and
control of node centrality in undirected graphs with asymmetric weight values.
In particular, we focus our attention on -centrality, which can be seen
as a generalization of eigenvector centrality. In this setting, we first
consider a distributed protocol where agents compute their -centrality,
focusing on the convergence properties of the method; then, we combine the
estimation method with a consensus algorithm to achieve a consensus value
weighted by the influence of each node in the network. Finally, we formulate an
-centrality control problem which is naturally decoupled and, thus,
suitable for a distributed setting and we apply this formulation to protect the
most valuable nodes in a network against a targeted attack, by making every
node in the network equally important in terms of {\alpha}-centrality.
Simulations results are provided to corroborate the theoretical findings.Comment: published on IEEE Transactions on Automatic Control
https://ieeexplore.ieee.org/abstract/document/912618
Dynamic max-consensus with local self-tuning
This work describes a novel control protocol for multi-agent systems to solve the dynamic max-consensus problem. In this problem, each agent has access to an external timevarying scalar signal and has the objective to estimate and track the maximum among all these signals by exploiting only local communications. The main strength of the proposed protocol is that it is able to self-tune its internal parameters in order to achieve an arbitrary small steady-state error without significantly affecting the convergence time. We employ the proposed protocol in the context of distributed graph parameter estimations, such as size, diameter, and radius, and provide simulations in the scenario of open multi-agent systems. Copyright (C) 2022 The Authors
A Distributed Asynchronous Method of Multipliers for Constrained Nonconvex Optimization
This paper presents a fully asynchronous and distributed approach for
tackling optimization problems in which both the objective function and the
constraints may be nonconvex. In the considered network setting each node is
active upon triggering of a local timer and has access only to a portion of the
objective function and to a subset of the constraints. In the proposed
technique, based on the method of multipliers, each node performs, when it
wakes up, either a descent step on a local augmented Lagrangian or an ascent
step on the local multiplier vector. Nodes realize when to switch from the
descent step to the ascent one through an asynchronous distributed logic-AND,
which detects when all the nodes have reached a predefined tolerance in the
minimization of the augmented Lagrangian. It is shown that the resulting
distributed algorithm is equivalent to a block coordinate descent for the
minimization of the global augmented Lagrangian. This allows one to extend the
properties of the centralized method of multipliers to the considered
distributed framework. Two application examples are presented to validate the
proposed approach: a distributed source localization problem and the parameter
estimation of a neural network.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0648
Distributed Finite-Time Cooperative Localization for Three-Dimensional Sensor Networks
This paper addresses the distributed localization problem for a network of
sensors placed in a three-dimensional space, in which sensors are able to
perform range measurements, i.e., measure the relative distance between them,
and exchange information on a network structure. First, we derive a necessary
and sufficient condition for node localizability using barycentric coordinates.
Then, building on this theoretical result, we design a distributed
localizability verification algorithm, in which we propose and employ a novel
distributed finite-time algorithm for sum consensus. Finally, we develop a
distributed localization algorithm based on conjugate gradient method, and we
derive a theoretical guarantee on its performance, which ensures finite-time
convergence to the exact position for all localizable nodes. The efficiency of
our algorithm compared to the existing ones from the state-of-the-art
literature is further demonstrated through numerical simulations.Comment: 39 pages, 7 figures, under revie
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