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Diffusion mechanisms for fixedpoint distributed kalman smoothing
 In EUSIPCO
, 2008
"... We consider the problem of fixedpoint distributed Kalman smoothing, where a set of nodes are required to estimate the initial condition of a certain process based on their measurements of the evolution of the process. Specifically, we consider linear statespace models where the Kalman smoother g ..."
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We consider the problem of fixedpoint distributed Kalman smoothing, where a set of nodes are required to estimate the initial condition of a certain process based on their measurements of the evolution of the process. Specifically, we consider linear statespace models where the Kalman smoother gives us the MMSE estimate of the initial state of the system. We propose distributed diffusion solutions where nodes communicate with their neighbors and information is propagated through the network via a diffusion process. Hierarchical cooperation schemes are also described. 1.
Distributed estimation through randomized gossip Kalman filter
 In Proc. 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference. Shanghai, pages 7049 7054
, 2009
"... Abstract—In this paper we consider the problem of estimating a random process from noisy measurements, collected by a sensor network. We analyze a distributed two–stage algorithm. The first stage is a Kalman–like estimate update, in which each agent makes use only of its own measurements. During th ..."
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Abstract—In this paper we consider the problem of estimating a random process from noisy measurements, collected by a sensor network. We analyze a distributed two–stage algorithm. The first stage is a Kalman–like estimate update, in which each agent makes use only of its own measurements. During the second phase agents communicate with their neighbors to improve their estimate. Estimate fusion is operated by running a consensus iteration. In literature it has been considered only the case of a fixed communication strategies, i.e. described by a fixed constant consensus matrix. However, in many practical cases this is just a rough model of communications in a sensor network, that usually happen according to a randomized strategy. This strategy is more properly modeled by assuming that the consensus matrices are drawn, according to a selection probability, from an alphabet of matrices compatible with the communication graph, at each time instant. This work deals therefore with randomized communication strategies and in particular with the symmetric gossip. A mean square performance analysis is carried out and an upper–bound for the trace of the estimation error variance is derived. The proposed upper–bound has to be considered the main technical contribution of the present paper, since it is based on a highly non–trivial inequality on matrix singular values, proved in the appendix. This upper–bound is a good performance assessment index and it is assumed therefore as a cost function to be minimized. We show moreover that problem of minimizing this cost function by choosing the Kalman gain and the selection probability is convex in each of the two variables separately although it is not jointly convex. Finally simulations are presented and the results discussed. I.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON MOBILE COMPUTING ESIP: Secure Incentive Protocol with Limited Use of Publi
"... Abstract—In multihop wireless networks, selfish nodes do not relay other nodes ’ packets and make use of the cooperative nodes to relay their packets, which has negative impact on the network fairness and performance. Incentive protocols use credits to stimulate the selfish nodes’ cooperation, but ..."
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Abstract—In multihop wireless networks, selfish nodes do not relay other nodes ’ packets and make use of the cooperative nodes to relay their packets, which has negative impact on the network fairness and performance. Incentive protocols use credits to stimulate the selfish nodes’ cooperation, but the existing protocols usually rely on the heavyweight publickey operations to secure the payment. In this paper, we propose secure cooperation incentive protocol that uses the publickey operations only for the first packet in a series and uses the lightweight hashing operations in the next packets, so that the overhead of the packet series converges to that of the hashing operations. Hash chains and keyed hash values are used to achieve payment nonrepudiation and thwart free riding attacks. Security analysis and performance evaluation demonstrate that the proposed protocol is secure and the overhead is incomparable to the publickey based incentive protocols because the efficient hashing operations dominate the nodes’ operations. Moreover, the average packet overhead is less than that of the publickey based protocols with very high probability due to truncating the keyed hash values. Index Terms — Networklevel security and protection, Mobile communication systems, Routing protocols, Payment schemes.
Review Article Recent Advances on Filtering and Control for Nonlinear Stochastic Complex Systems with Incomplete Information: A Survey
, 2011
"... Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Some recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The i ..."
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Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Some recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out. 1.