Distributed optimization in sensor networks

Rabbat, Michael; Nowak, Robert

doi:10.1145/984622.984626

Cited by 783 publications

(644 citation statements)

References 12 publications

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“…• Remark 5.7 (Discrete-time counterpart of (6) and (11)): It is worth noticing that the discretization of (6) for undirected graphs (performed in [12] for the case of continuously differentiable, strictly convex functions) and (11) for weight-balanced digraphs gives rise to different discretetime optimization algorithms from the ones considered in [1], [2], [3], [4], [5], [6].…”

Section: Lemma 53mentioning

confidence: 99%

Distributed Continuous-Time Convex Optimization on Weight-Balanced Digraphs

Gharesifard

Cortés

2014

IEEE Trans. Automat. Contr.

706

377

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This paper studies the continuous-time distributed optimization of a sum of convex functions over directed graphs. Contrary to what is known in the consensus literature, where the same dynamics works for both undirected and directed scenarios, we show that the consensus-based dynamics that solves the continuous-time distributed optimization problem for undirected graphs fails to converge when transcribed to the directed setting. This study sets the basis for the design of an alternative distributed dynamics which we show is guaranteed to converge, on any strongly connected weightbalanced digraph, to the set of minimizers of a sum of convex differentiable functions with globally Lipschitz gradients. Our technical approach combines notions of invariance and cocoercivity with the positive definiteness properties of graph matrices to establish the results.

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Section: Lemma 53mentioning

confidence: 99%

Distributed Continuous-Time Convex Optimization on Weight-Balanced Digraphs

Gharesifard

Cortés

2014

IEEE Trans. Automat. Contr.

706

377

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“…The schemes [71,72,73] require specific routing services from the network, e.g. formation and maintenance of cyclic paths that pass through each node in the network.…”

Section: Decentralized Computation In Wireless Sensor Networkmentioning

confidence: 99%

Distributed ensemble Kalman filtering

Shahid

Üstebay

Coates

2014

2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Distributed estimation in a wireless sensor network has many advantages. It eliminates the need of the centralized knowledge of the measurement model parameters and does not have a single point of failure. Also, any sensor node can be queried to retrieve the state estimate. Furthermore, for high dimensional measurements, local processing of information also results in a significant reduction in the communication overhead.The existing distributed Kalman filtering schemes have good computational and communication efficiency, but do not work well for non-linear and non-Gaussian problems. On the other hand, the distributed particle filtering schemes handle the non-linearities well, but have a much higher computational and communication cost.In this thesis, we propose novel distributed filtering schemes based on the ensemble Kalman filter (EnKF). We consider three forms of the EnKF and express their update equations in an alternative information form. This allows us to use the randomized gossip algorithm to reach consensus on the sufficient statistics and perform local updates. The simulation results show that all three forms of the EnKF have a considerably lower computational cost compared to the particle filters. The results suggest that the proposed distributed schemes outperform the existing state-of-the-art distributed filtering schemes in two scenarios, a) linear measurement model with non-linear state dynamics, and b) high dimensional measurements (model parameters known everywhere in the network) with nonlinear measurement model and state dynamics. In both of these scenarios, the proposed schemes achieve an estimation accuracy comparable to the existing state-of-the-art schemes while significantly reducing the communication cost.ii Résumé L'estimation distribuée dans un réseau de capteurs sans fil possède plusieurs avantages. Ellé elimine le besoin d'une connaissance centralisée des paramtres du modèle de mesure et n'a pas un point de défaillance unique. Aussi, n'importe quel agent-capteur peut-être consulté pour obtenir une approximation de l'état général. De plus, pour des mesures de hautes dimensions, le calcul local d'informations résulte en une réduction significative des coûts de communication.Les implémentations courantes du filtre de Kalman sont efficaces sur les plans de la charge de calcul et de la communication mais ne le sont pas pour les problmes non-linéaires et non-gaussiens. D'un autre côté, les techniques distribuées de filtrage particulaire gèrent avec succès les cas non-linéaires mais sont coûteuses sur les plans de la charge de calcul et de la communication.Dans cette thèse, nous proposons des techniques de filtrage distribué basées sur le filtre de Kalman d'ensemble (FKEn). Nous considérons trois formes du FKEn et exprimons leurséquations de changement sous une forme alternative. Cela nous permet d'utiliser un algorithme de gossip aléatoire afin d'atteindre un consensus sur les statistiques suffisantes et calculer les changements locaux. Les résultats des simulations montrent que ...

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“…Nevertheless, the energy consumption in distributed algorithms may get higher if several iterations are needed to reach a stable estimation of the position [57]. A comparative performance study between distributed and centralized approaches has been presented in [67] and showed that the distributed algorithms are much more efficient, in terms of energy and communication than centralized estimation schemes using both analytical and simulation models. Table 2 summarizes the major differences between distributed and centralized localization approaches.…”

Section: Algorithmsmentioning

confidence: 99%

Taxonomy of Fundamental Concepts of Localization in Cyber-Physical and Sensor Networks

Koubâa

Jamâa²

2013

Wireless Pers Commun

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Localization is a fundamental task in Cyber-Physical Systems (CPS), where data is tightly coupled with the environment and the location where it is generated. The research literature on localization has reached a critical mass, and several surveys have also emerged. This review paper contributes on the state-of-the-art with the proposal of a new and holistic taxonomy of the fundamental concepts of localization in CPS, based on a comprehensive analysis of previous research works and surveys. The main objective is to pave the way towards a deep understanding of the main localization techniques, and unify their descriptions. Furthermore, this review paper provides a complete overview on the most relevant localization and geolocation techniques. Also, we present the most important metrics for measuring the accuracy of localization approaches, which is meant to be the gap between the real location and its estimate. Finally, we present open issues and research challenges pertaining to localization. We believe that this review paper will represent an important and complete reference of localization techniques in CPS for researchers and practitioners and will provide them with an added value as compared to previous surveys.

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Distributed optimization in sensor networks

Cited by 783 publications

References 12 publications

Distributed Continuous-Time Convex Optimization on Weight-Balanced Digraphs

Distributed Continuous-Time Convex Optimization on Weight-Balanced Digraphs

Distributed ensemble Kalman filtering

Taxonomy of Fundamental Concepts of Localization in Cyber-Physical and Sensor Networks

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