20XX
A. Carron, M. Todescato, R. Carli, L. Schenato.
An asynchronous consensus-based algorithm for estimation from noisy relative measurements. IEEE Transactions on Control of Network Systems (submitted), 20XX [
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BibTeX]
A. Chiuso, N. Laurenti, L. Schenato, A. Zanella.
Analysis of delay-throughput-reliability tradeoff in a multihop wireless channel for the control of unstable systems. ICC 2014, 20XX [
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BibTeX]
R. Carli, G. Notarstefano, L. Schenato, D. Varagnolo.
Asynchronous Newton-Raphson Consensus for Robust Distributed Convex Optimization. IEEE Conference on Decision and Control (CDC14), 20XX [
pdf] [
BibTeX]
M. Todescato, A. Carron, R. Carli, L. Schenato.
Distributed Localization from Relative Noisy Measurements: a Robust Gradient Based Approach. IEEE Conference on Decision and Control (CDC14), 20XX [
pdf] [
BibTeX]
L. Brinon-Arranz, L. Schenato.
Distributed Source-seeking via a Circular Formation of Agents with asynchronous communication. IEEE Transactions on Control of Network Systems (submitted), 20XX [
BibTeX]
A. Chiuso, N. Laurenti, L. Schenato, A. Zanella.
LQG control over communication channels: the role of data losses, delays and SNR limitations. Automatica (submitted), 20XX [
pdf] [
BibTeX]
A. Carron, M. Todescato, R. Carli, L. Schenato, G. Pillonetto.
Multi-agents adaptive estimation and coverage control using Gaussian regression. IEEE Conference on Decision and Control (CDC14), 20XX [
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D. Varagnolo, F. Zanella, A. Cenedese, G. Pillonetto, L. Schenato.
Newton-Raphson Consensus for Distributed Convex Optimization. IEEE Transactions on Automatic Control (submitted), 20XX
Abstract:
We address the problem of distributed unconstrained convex optimization under separability assumptions, i.e., the framework where a network of agents, each endowed with local private multidimensional convex cost and subject to communication constraints, wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proven, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton-Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.
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K. Yildirim, R. Carli, L. Schenato.
Proportional-Integral Synchronization In Wireless Sensor Networks. ACM Transactions on Sensor Networks (submitted), 20XX [
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BibTeX]
S. Dey, A. Chiuso, L. Schenato.
Remote estimation with noisy measurements subject to packet loss and quantization noise. IEEE Transactions on Control of Network Systems (submitted), 20XX [
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2014
D. Varagnolo, G. Pillonetto, L. Schenato.
Distributed cardinality estimation in anonymous networks. IEEE Transactions on Automatic Control, vol. 59(3), pp. 645-659, 2014
Abstract:
The knowledge of the size of a network, i.e.\ of the number of nodes composing it, is important for maintenance and organization purposes. In networks where the identity of the nodes or is not unique or cannot be disclosed for privacy reasons, the size-estimation problem is particularly challenging since the exchanged messages cannot be uniquely associated with a specific node. In this work, we propose a totally distributed anonymous strategy based on statistical inference concepts. In our approach, each node starts generating a vector of independent random numbers from a known distribution. Then nodes compute a common function via some distributed consensus algorithms, and finally they compute the Maximum Likelihood (ML) estimate of the network size exploiting opportune statistical inferences. In this work we study the performance that can be obtained following this computational scheme when the consensus strategy is either the maximum or the average. In the max-consensus scenario, when data come from absolutely continuous distributions, we provide a complete characterization of the ML estimator. In particular, we show that the squared estimation error decreases as $1/M$, where $M$ is the amount of random numbers locally generated by each node, independently of the chosen probability distribution. Differently, in the average-consensus scenario, we show that if the locally generated data are independent Bernoulli trials, then the probability for the ML estimator to return a wrong answer decreases exponentially in $M$. Finally, we provide a discussion as how the numerical errors may affect the estimators performance under different scenarios.
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2013
D. Varagnolo, L. Schenato, G. Pillonetto.
A variation of the Newton-Pepys problem and its connections to size-estimation problems. Statistics & Probability Letters, (83), pp. 1472-1478, 2013
Abstract:
This paper considers a variation of the 17$^{\text{th}}$ century problem commonly known as the Newton-Pepys problem, or the John Smith's problem. We provide its solution, interpreting the result in terms of maximum likelihood estimation and Ockham's razor. In addition, we illustrate the practical relevance of these findings for solving size-estimation problems, and in particular for determining the number of agents in a wireless sensor network.
[ abstract ] [
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A. Carron, M. Todescato, R. Carli, L. Schenato.
Adaptive consensus-based algorithms for fast estimation from relative measurements. 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys'13), 2013 [
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R. Carli, A. Carron, L. Schenato, M. Todescato.
An exponential-rate consensus-based algorithms for estimation from relative measurements: implementation and performance analysis. 2013 [
pdf] [
BibTeX]
L. Brinon-Arranz, L. Schenato.
Consensus-based Source-seeking with a Circular Formation of Agents. European Control Conference ECC13, 2013 [
pdf] [
BibTeX]
D. Varagnolo, S. Del Favero, F. Dinuzzo, L. Schenato, G. Pillonetto.
Finding Potential Support Vectors in linearly separable classification problems. IEEE Transactions on Neural Networks and Learning Systems, vol. 24(11), pp. 1799-1813, 2013
Abstract:
The paper considers the classification problem using Support Vector Machines, and investigates how to maximally reduce the size of the training set without losing information. Under linearly separable dataset assumptions, we derive the exact conditions stating which observations can be discarded without diminishing the overall information content. For this purpose, we introduce the concept of Potential Support Vectors, i.e., those data that can become Support Vectors when future data become available. Complementary, we also characterize the set of Discardable Vectors, i.e., those data that, given the current dataset, can never become Support Vectors. These vectors are thus useless for future training purposes, and can eventually be removed without loss of information. We then provide an efficient algorithm based on linear programming which returns the potential and discardable vectors by constructing a simplex tableau. Finally we compare it with alternative algorithms available in the literature on some synthetic data as well as on datasets from standard repositories.
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S. Bolognani, N. Bof, D. Michelotti, R. Muraro, L. Schenato.
Identification of power distribution network topology via voltage correlation analysis. Conference on Decision and Control (CDC13), 2013 [
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F. Parise, L. Dal Col, A. Chiuso, N. Laurenti, L. Schenato, A. Zanella.
Impact of a realistic transmission channel on the performance of control systems. 2013 [
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A. Chiuso, N. Laurenti, L. Schenato, A. Zanella.
LQG cheap control over SNR-limited lossy channels with delay. Conference on Decision and Control (CDC13), 2013 [
pdf] [
BibTeX]
A. Chiuso, N. Laurenti, L. Schenato, A. Zanella.
LQG cheap control subject to packet loss and SNR limitations. European Control Conference ECC13, 2013 [
pdf] [
BibTeX]
S. Dey, A. Chiuso, L. Schenato.
Remote estimation subject to packet loss and quantization noise. Conference on Decision and Control (CDC13), 2013 [
pdf] [
BibTeX]
2012
F. Zanella, D. Varagnolo, A. Cenedese, G. Pillonetto, L. Schenato.
Asynchronous Newton-Raphson Consensus for Distributed Convex Optimization. 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys'12), 2012
Abstract:
We consider the distributed unconstrained minimization of separable convex costfunctions, where the global cost is given by the sum of several local and private costs, eachassociated to a specific agent of a given communication network. We specifically address anasynchronous distributed optimization technique called Newton-Raphson consensus. Besidehaving low computational complexity, low communication requirements and being interpretableas a distributed Newton-Raphson algorithm, the technique has also the beneficial properties ofrequiring very little coordination and naturally support time-varying topologies. In this workwe analytically prove that under some assumptions it shows local convergence properties, andcorroborate this result by means of numerical simulations.
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D. Varagnolo, G. Pillonetto, L. Schenato.
Consensus based estimation of anonymous networks size using Bernoulli trials. 2012 American Control Conference, 2012
Abstract:
To maintain and organize distributed systems it is necessary to have a certain degree of knowledge of their status like the number of cooperating agents. The estimation of this number, usually referred as the network size, can pose challenging questions when agents' identification information cannot be disclosed, since the exchanged information cannot be associated to who originated it. In this paper we propose a totally distributed network size estimation strategy based on statistical inference concepts that can be applied under anonymity constraints. The scheme is based on the following paradigm: agents locally generate some Bernoulli trials, then distributedly compute averages of these generated data, finally locally compute the Maximum Likelihood estimate of the network size exploiting its probabilistic dependencies with the previously computed averages. In this work we study the statistical properties of this estimation strategy, and show how the probability of returning a wrong evaluation decreases exponentially in the number of locally generated trials. Finally, we discuss how practical implementation issues may affect the estimator, and show that there exists a neat phase transition between insensitivity to numerical errors and uselessness of the results.
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S. Bolognani, A. Carron, A. Di Vittorio, D. Romeres, L. Schenato, S. Zampieri.
Distributed multi-hop reactive power compensation in smart micro-grids subject to saturation constraints. Conference on Decision and Control,
Proceedings of CDC 2012, 2012
Abstract:
In this paper we address the problem of exploitingthe distributed energy resources (DER) available in a smartmicro-grid to minimize the power distribution losses via optimalreactive power compensation. Due to their typically smallsize, the amount of reactive power provided by each micro-generator is subject to tight saturation constraints. As aconsequence, it might be impossible to achieve convergence tothe global optimum based on algorithms that rely on short-range, gossip-type communication. We therefore propose arandomized multi-hop protocol that guarantees convergence ofthe distributed optimization algorithm also when only short-range communications are possible, at the expense of someadditional communication overhead.
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D. Varagnolo, G. Pillonetto, L. Schenato.
Distributed parametric and nonparametric regression with on-line performance bounds computation. Automatica, vol. 48(10), pp. 2468 -- 2481, 2012
Abstract:
In this paper we focus on collaborative wireless sensor networks, where agents are randomly distributed over a region of interest and collaborate to achieve a common estimation goal. In particular, we introduce two consensus-based distributed linear estimators. The first one is designed for a Bayesian scenario, where an unknown common finite-dimensional parameter vector has to be reconstructed, while the second one regards the nonparametric reconstruction of an unknown function sampled at different locations by the sensors. Both of the algorithms are characterized in terms of the trade-off between estimation performance, communication, computation and memory complexity. In the finite-dimensional setting, we derive mild sufficient conditions which ensure that distributed estimator performs better than the local optimal ones in terms of estimation error variance. In the nonparametric setting, we introduce an on-line algorithm that allows the agents both to compute the function estimate with small computational, communication and data storage efforts, and to quantify its distance from the centralized estimate given by a Regularization Network, one of the most powerful regularized kernel methods. These results are obtained by deriving bounds on the estimation error that provide insights on how the uncertainty inherent in a sensor network, such as imperfect knowledge on the number of agents and the measurement models used by the sensors, can degrade the performance of the estimation process. Numerical experiments are included to support the theoretical findings.
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R. Alberton, R. Carli, A. Cenedese, L. Schenato.
Multi-agent perimeter patrolling subject to mobility constraints. Proceedings of American Control Conference ACC2012, 2012
Abstract:
In this paper we study the problem of real-time optimal distributed
partitioning for perimeter patrolling in the context of multi-camera
networks for surveillance. The objective is to partition a given segment
into non-overlapping sub-segments, each assigned to a different camera
to patrol. Each camera has both physical mobility range and limited
speed, and it must patrol its assigned sub-segment by sweeping it back
and forth at maximum speed. Here we first review the solution for the
centralized optimal partitioning. Then we propose two different
distributed control strategies to determine the extremes of the optimal
patrolling areas of each camera. Both these strategies require only
local communication with the neighboring cameras but adopt different
communication schemes, respectively, symmetric gossip and asynchronous
asymmetric broadcast. The first scheme is shown to be provably
convergent to the optimal solution. Some theoretical insights are
provided also for the second scheme whose effectiveness is validated
through numerical simulations.
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F. Zanella, D. Varagnolo, A. Cenedese, G. Pillonetto, L. Schenato.
Multidimensional Newton-Raphson consensus for distributed convex optimization. 2012 American Control Conference, 2012
Abstract:
In this work we consider a multidimensional distributed optimization technique that is suitable for multiagents systems subject to limited communication connectivity. In particular, we consider a convex unconstrained additive problem, i.e. a case where the global convex unconstrained multidimensional cost function is given by the sum of local cost functions available only to the specific owning agents. We show how, by exploiting the separation of time-scales principle,the multidimensional consensus-based strategy approximates a Newton-Raphson descent algorithm. We propose two alternative optimization strategies corresponding to approximations of the main procedure. These approximations introduce tradeoffs between the required communication bandwidth and the convergence speed/accuracy of the results. We provide analytical proofs of convergence and numerical simulations supporting the intuitions developed through the paper.
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F. Zanella, D. Varagnolo, A. Cenedese, G. Pillonetto, L. Schenato.
The convergence rate of Newton-Raphson consensus optimization for quadratic cost functions. IEEE Conference on Decision and Control (CDC 2012), 2012
Abstract:
We consider the convergence rates of two peculiar2 convex optimization strategies in the context of multi agent3 systems, namely the Newton-Raphson consensus optimization4 and a distributed Gradient-Descent opportunely derived from5 the first. To allow analytical derivations, the convergence6 analyses are performed under the simplificative assumption of7 quadratic local cost functions. In this framework we derive8 sufficient conditions which guarantee the convergence of the9 algorithms. From these conditions we then obtain closed form10 expressions that can be used to tune the parameters for11 maximizing the rate of convergence. Despite these formulae12 have been derived under quadratic local cost functions13 assumptions, they can be used as rules-of-thumb for tuning14 the parameters of the algorithms in general situations.
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