Decentralized Maximum Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems

In this paper we propose a decentralized sensor network scheme capable to reach a globally optimum maximum likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network and we validate our theoretical findings with simulation results.Comment: Journal paper accepted on IEEE Transactions on Signal Processin

Distributed Learning from Interactions in Social Networks

We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction set-up for user profiling.Comment: This submission is a shorter work (for conference publication) of a more comprehensive paper, already submitted as arXiv:1706.04081 (under review for journal publication). In this short submission only one social set-up is considered and only one of the relaxed estimators is proposed. Moreover, the exhaustive analysis, carried out in the longer manuscript, is completely missing in this versio

Vulnerability analysis of satellite-based synchronized smart grids monitoring systems

The large-scale deployment of wide-area monitoring systems could play a strategic role in supporting the evolution of traditional power systems toward smarter and self-healing grids. The correct operation of these synchronized monitoring systems requires a common and accurate timing reference usually provided by a satellite-based global positioning system. Although these satellites signals provide timing accuracy that easily exceeds the needs of the power industry, they are extremely vulnerable to radio frequency interference. Consequently, a comprehensive analysis aimed at identifying their potential vulnerabilities is of paramount importance for correct and safe wide-area monitoring system operation. Armed with such a vision, this article presents and discusses the results of an experimental analysis aimed at characterizing the vulnerability of global positioning system based wide-area monitoring systems to external interferences. The article outlines the potential strategies that could be adopted to protect global positioning system receivers from external cyber-attacks and proposes decentralized defense strategies based on self-organizing sensor networks aimed at assuring correct time synchronization in the presence of external attacks

Graph Laplacian based Matrix Design for Finite-Time Distributed Average Consensus

International audienceIn this paper, we consider the problem of finding a linear iteration scheme that yields distributed average consensus in a finite number of steps D. By modeling interactions between the nodes in the network by means of a time-invariant undirected graph, the problem is solved by deriving a set of D Laplacian based consensus matrices. We show that the number of steps is given by the number of nonzero distinct eigenvalues of the graph Laplacian matrix. Moreover the inverse of these eigenvalues constitute the step-sizes of the involved Laplacian based consensus matrices. When communications are made through an additive white Gaussian noise channel, based on an ensemble averaging method, we show how average consensus can be asymptotically reached. Performance analysis of the suggested protocol is given along with comparisons with other methods in the literature