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Consensus networks over finite fields
, 2013
"... This work studies consensus strategies for networks of agents with limited memory, computation, and communication capabilities. We assume that agents can process only values from a finite alphabet, and we adopt the framework of finite fields, where the alphabet consists of the integers {0,..., p − ..."
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This work studies consensus strategies for networks of agents with limited memory, computation, and communication capabilities. We assume that agents can process only values from a finite alphabet, and we adopt the framework of finite fields, where the alphabet consists of the integers {0,..., p − 1}, for some prime number p, and operations are performed modulo p. Thus, we define a new class of consensus dynamics, which can be exploited in certain applications such as pose estimation in capacity and memory constrained sensor networks. For consensus networks over finite fields, we provide necessary and sufficient conditions on the network topology and weights to ensure convergence. We show that consensus networks over finite fields converge in finite time, a feature that can be hardly achieved over the field of real numbers. For the design of finitefield consensus networks, we propose a general design method, with high computational complexity, and a network composition rule to generate large consensus networks from smaller components. Finally, we discuss the application of finitefield consensus networks to distributed averaging and pose estimation in sensor networks.
Grenoble RhôneAlpes THEME Modeling, Optimization, and Control of Dynamic SystemsTable of contents
"... 3.1. Dynamic nonregular systems 2 3.2. Nonsmooth optimization 3 ..."
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THEME Modeling, Optimization, and Control of Dynamic SystemsTable of contents
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A variation of the NewtonPepys problem and its connections to sizeestimation problems
"... This paper considers a variation of the 17th century problem commonly known as the NewtonPepys problem, or the John Smith’s problem. We provide its solution and interpret the result in terms of maximum likelihood estimation. In addition, we illustrate the practical relevance of these findings for s ..."
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This paper considers a variation of the 17th century problem commonly known as the NewtonPepys problem, or the John Smith’s problem. We provide its solution and interpret the result in terms of maximum likelihood estimation. In addition, we illustrate the practical relevance of these findings for solving sizeestimation problems, and in particular for determining the number of agents in a wireless sensor network.
Multiagent systems Distributed consensus Synchronization
, 2015
"... li • Velocities converge sufficiently fast so that distances between agents are bounded. a r t i c l e i n f o Article history: ..."
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li • Velocities converge sufficiently fast so that distances between agents are bounded. a r t i c l e i n f o Article history:
How to Implement DoublyStochastic Matrices for ConsensusBased Distributed Algorithms
"... This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at ..."
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This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at
Rigid Motion
"... Abstract — This paper proposes a distributed optimization algorithm for estimation of spatial rigid motion using multiple image sensors in a connected network. The objective is to increase the estimation precision of translational and rotational motion based on dual quaternion models and cooperation ..."
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Abstract — This paper proposes a distributed optimization algorithm for estimation of spatial rigid motion using multiple image sensors in a connected network. The objective is to increase the estimation precision of translational and rotational motion based on dual quaternion models and cooperation between connected sensors. The distributed Newton optimization method is applied to decompose the filtering task into a series of suboptimal problems and then solve them individually to achieve the global optimality. Our approach assumes that each sensor can communicate with its neighboring sensors to update the individual estimates. Simulation examples are demonstrated to compare the proposed algorithm with other methods in terms of estimation accuracy and converging rate.
1On the Role of Network Centrality in the Controllability of Complex Networks
"... Abstract—In recent years complex networks have gained increasing attention in different fields of science and engineering. The problem of controlling these networks is an interesting and challenging problem to investigate. In this paper we look at the controllability problem focusing on the energy ..."
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Abstract—In recent years complex networks have gained increasing attention in different fields of science and engineering. The problem of controlling these networks is an interesting and challenging problem to investigate. In this paper we look at the controllability problem focusing on the energy needed for the control. Precisely not only we want to analyze whether a network can be controlled, but we also want to establish whether the control can be performed using a limited amount of energy. We restrict our study to irreducible and (marginally) stable networks and we find that the leading right and left eigenvectors of the network matrix play a crucial role in this analysis. Interestingly, our results suggest the existence of a connection between controllability and network centrality, a wellknown concept in network science. In case the network is reversible, the latter connection involves the PageRank, an extensively studied type of centrality measure. Finally, the proposed results are applied to examples concerning random graphs. Index Terms—Complex networks, controllability, network centrality, PageRank. I.