This paper introduces a stochastic approach that considers the distributed classification problem for a network of underwater acoustic sensors. The proposed classifier applies the third order polynomial regression to ...
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ISBN:
(纸本)9781479936465
This paper introduces a stochastic approach that considers the distributed classification problem for a network of underwater acoustic sensors. The proposed classifier applies the third order polynomial regression to the instantaneous frequency extracted from time-frequency representation of different classes of signals and represent the polynomial's coefficients in a three-dimensional representation space. This automatic classifier is then compared to a non-parametric classifier based on the training of a standard neural network. The results of the proposed method on real data illustrate the efficiency of this algorithm, in terms of signal's characterization and lower communication bit rates between each sensor and the data center.
We consider a convex optimization problem for non-hierarchical agent networks where each agent has access to a local or private time-varying function, and the network-wide objective is to find a time-invariant minimiz...
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We consider a convex optimization problem for non-hierarchical agent networks where each agent has access to a local or private time-varying function, and the network-wide objective is to find a time-invariant minimizer of the sum of these functions, provided that such a minimizer exists. Problems of this type are common in dynamic systems where the objective function is time-varying because of, for instance, the dependency on measurements that arrive continuously to each agent. A typical outer-loop optimization iteration for optimization problems of this type consists of a local optimization step based on the information provided by neighboring agents, followed by a consensus step to exchange and fuse local estimates of the agents. A great deal of research effort has been directed towards developing and better understanding such algorithms, which find many applications in distinct areas such as cognitive radio networks, distributed acoustic source localization, coordination of unmanned vehicles, and environmental modeling. Contrasting with existing work, which considers either dynamic systems or noisy links (but not both jointly), in this study we devise and analyze a novel distributed online algorithm for dynamic optimization problems in noisy communication environments. The main result of the study proves sufficient conditions for almost sure convergence of the algorithm as the number of iterations tends to infinity. The algorithm is applicable to a wide range of distributed optimization problems with time-varying cost functions and consensus updates corrupted by additive noise. Our results therefore extend previous work to include recently proposed schemes that merge the processes of computation and data transmission over noisy wireless networks for fast and efficient consensus protocols. To give a concrete example of an application, we show how to apply our general technique to the problem of distributed detection with adaptive filters.
We study a general framework for broadcast gossip algorithms which use companion variables to solve the average consensus problem. Each node maintains an initial state and a companion variable. Iterative updates are p...
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We study a general framework for broadcast gossip algorithms which use companion variables to solve the average consensus problem. Each node maintains an initial state and a companion variable. Iterative updates are performed asynchronously whereby one random node broadcasts its current state and companion variables and all other nodes receiving the broadcast update their state and companion variables. We provide conditions under which this scheme is guaranteed to converge to a consensus solution, where all nodes have the same limiting values, on any strongly connected directed graph. Under stronger conditions, which are reasonable when the underlying communication graph is undirected, we guarantee that the consensus value is equal to the average, both in expectation and in the mean-squared sense. Our analysis uses tools from non-negative matrix theory and perturbation theory. The perturbation results rely on a parameter being sufficiently small. We characterize the allowable upper bound as well as the optimal setting for the perturbation parameter as a function of the network topology, and this allows us to characterize the worst-case rate of convergence. Simulations illustrate that, in comparison to existing broadcast gossip algorithms, the approaches proposed in this paper have the advantage that they simultaneously can be guaranteed to converge to the average consensus and they converge in a small number of broadcasts.
This paper describes and analyzes a hierarchical algorithm called Multiscale Gossip for solving the distributed average consensus problem in wireless sensor networks. The algorithm proceeds by recursively partitioning...
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This paper describes and analyzes a hierarchical algorithm called Multiscale Gossip for solving the distributed average consensus problem in wireless sensor networks. The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest scale gossip to compute local averages. Then, using multi-hop communication and geographic routing to communicate between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed. We show that the proposed hierarchical scheme with k = Theta(log log n) levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms in terms of message complexity, achieving epsilon-accuracy with high probability after O(n log log n log 1/epsilon) single-hop messages. Key to our analysis is the way in which the network is recursively partitioned. We find that the above scaling law is achieved when subnetworks at scale j contain O(n((2/3)j)) nodes;then the message complexity at any individual scale is O(n log 1/epsilon) Another important consequence of the hierarchical construction is that the longest distance over which messages are exchanged is O(n(1/3)) hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms based on averaging along paths.
Motivated by the decentralized adaptive resource management problems, the letter derives recursive expressions for online computation of the conditional decentralized posterior Cramer-Rao lower bound (PCRLB). Compared...
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Motivated by the decentralized adaptive resource management problems, the letter derives recursive expressions for online computation of the conditional decentralized posterior Cramer-Rao lower bound (PCRLB). Compared to the non-conditional PCRLB, the conditional PCRLB is a function of the past history of observations made and, therefore, a more accurate representation of the estimator's performance and, consequently, a better criteria for sensor selection. Previous algorithms to compute the conditional PCRLB are limited to centralized architectures. The letter addresses this gap. Our simulations verify the optimality of the conditional dPCRLB by comparing it with the centralized conditional PCRLB in bearing-only tracking applications.
A repeated network game where agents' utilities depend on information and payoff externalities is considered. Agents play Bayesian Nash Equilibrium strategies with respect to their beliefs on the state of the worl...
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ISBN:
(纸本)9781479903566
A repeated network game where agents' utilities depend on information and payoff externalities is considered. Agents play Bayesian Nash Equilibrium strategies with respect to their beliefs on the state of the world and the actions of all other nodes in the network. These beliefs are refined over subsequent stages based on the observed actions of neighboring peers. This paper introduces the Quadratic Network Game (QNG) filter that agents can run locally to update their beliefs, select corresponding optimal actions, and eventually learn a sufficient statistic of the network's state. The QNG filter is demonstrated on a coordination game.
Underwater acoustic sensor networks are an important element in a large number of applications such as surveillance, environment monitoring. The development of an autonomous sensor system imposes the distribution of t...
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ISBN:
(纸本)9780933957404
Underwater acoustic sensor networks are an important element in a large number of applications such as surveillance, environment monitoring. The development of an autonomous sensor system imposes the distribution of the data processing at the level of sensors. This operation is subject to a trade-off between the processing efficiency and the algorithmic complexity that influences the power consumption. In the case of current networks, a common way to answer to this trade-off issue is to process data (detection, recording, transmission.) when the energy of the observed signal, in an arbitrary analysis window, exceeds a given threshold. In this paper, we propose an underwater network configuration that is composed of sensors implementing a different processing approach, based on the hypothesis of a local time-frequency coherence (FM sweep). The results obtained on real data illustrate the efficiency of this algorithm, in terms of signal's characterization and lower communication bit rates.
Data-driven, adaptive computations are key to enabling the deployment of accurate and efficient stream mining systems, which invoke suitably configured queries in real-time on streams of input data. Due to the physica...
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Data-driven, adaptive computations are key to enabling the deployment of accurate and efficient stream mining systems, which invoke suitably configured queries in real-time on streams of input data. Due to the physical separation among data sources and computational resources, it is often necessary to deploy such stream mining systems in a distributed fashion, where local learners have access to disjoint subsets of the data that is to be mined, and forward their intermediate results to an ensemble learner that combines the results from the local learners. In this paper, we develop a design methodology for integrated de- sign, simulation, and implementation of dynamic data-driven adaptive stream mining systems. By systematically integrating considerations associated with local embedded processing, classifier configuration, data-driven adaptation and networked com- munication, our approach allows for effective assessment, prototyping, and implementation of alternative distributed design methods for data-driven, adaptive stream mining systems. We demonstrate our results on a dynamic data-driven application involving patient health care monitoring.
We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient distributed signal processing methods such...
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We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient distributed signal processing methods such as belief propagation (BP). The classical maximum likelihood approach to this covariance estimation problem, or potential function estimation in BP terminology, requires centralized computing and is computationally intensive. This motivates suboptimal distributed alternatives that tradeoff accuracy for communication cost. A natural solution is for each node to perform estimation of its local covariance with respect to its neighbors. The local maximum likelihood estimator is asymptotically consistent but suboptimal, i.e., it does not minimize mean squared estimation (MSE) error. We propose to improve the MSE performance by introducing additional symmetry constraints using averaging and pseudolikelihood estimation approaches. We compute the proposed estimates using message passing protocols, which can be efficiently implemented in large scale graphical models with many nodes. We illustrate the advantages of our proposed methods using numerical experiments with synthetic data as well as real world data from a wireless sensor network.
In wireless sensor network each sensor node collects data related to some unknown parameters, corrupted by independent Gaussian noise. Then the objective is to estimate the parameter from the data collected across the...
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ISBN:
(纸本)9783642280726;9783642280733
In wireless sensor network each sensor node collects data related to some unknown parameters, corrupted by independent Gaussian noise. Then the objective is to estimate the parameter from the data collected across the network in distributed manner. The distributed estimation algorithm should be energy efficient, provides high estimation accuracy, and is fast in convergence. But the conventional distributed algorithm involves significant communication overhead and is also not robust to the impulsive noise which is common in wireless sensor network environment. Consequently these algorithms defeat the basic purpose of wireless sensor network. This paper studies the problem of robust adaptive estimation in impulsive noise environment using robust cost function like Wilcoxon norm and Huber cost function. Further in order to reduce the amount of communication overhead, block distributed LMS is incorporated.
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