68 research outputs found
Adaptive diffusion schemes for heterogeneous networks
In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i.e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size. Although such heterogeneous networks have been considered from the first works on diffusion networks, obtaining practical and robust schemes to adaptively adjust the combiners in different scenarios is still an open problem. In this paper, we study a diffusion strategy specially designed and suited to heterogeneous networks. Our approach is based on two key ingredients: 1) the adaptation and combination phases are completely decoupled, so that network nodes keep purely local estimations at all times and 2) combiners are adapted to minimize estimates of the network mean-square-error. Our scheme is compared with the standard adapt-Then-combine scheme and theoretically analyzed using energy conservation arguments. Several experiments involving networks with heterogeneous nodes show that the proposed decoupled adapt-Then-combine approach with adaptive combiners outperforms other state-of-The-Art techniques, becoming a competitive approach in these scenarios
Distributed adaptive estimation based on the APA algorithm over diffusion networks with changing topology
In this paper, we present a novel distributed affine projection algorithm (APA) to solve distributed estimation problem within dynamic diffusion networks. In addition, mean-square stability of the proposed algorithm is also studied through exploitation of the energy conservation approach due to Sayed. Simulations confirm that the novel algorithm achieves a greatly improved performance as compared with a noncooperative scheme
Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation
A distributed adaptive algorithm is proposed to solve a node-specific
parameter estimation problem where nodes are interested in estimating
parameters of local interest, parameters of common interest to a subset of
nodes and parameters of global interest to the whole network. To address the
different node-specific parameter estimation problems, this novel algorithm
relies on a diffusion-based implementation of different Least Mean Squares
(LMS) algorithms, each associated with the estimation of a specific set of
local, common or global parameters. Coupled with the estimation of the
different sets of parameters, the implementation of each LMS algorithm is only
undertaken by the nodes of the network interested in a specific set of local,
common or global parameters. The study of convergence in the mean sense reveals
that the proposed algorithm is asymptotically unbiased. Moreover, a
spatial-temporal energy conservation relation is provided to evaluate the
steady-state performance at each node in the mean-square sense. Finally, the
theoretical results and the effectiveness of the proposed technique are
validated through computer simulations in the context of cooperative spectrum
sensing in Cognitive Radio networks.Comment: 13 pages, 6 figure
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Analysis of incremental augmented affine projection algorithm for distributed estimation of complex-valued signals
This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD). We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations
Algorithms for energy-efficient adaptive wireless sensor networks
Mención Internacional en el título de doctorIn this thesis we focus on the development of energy-efficient adaptive algorithms for Wireless Sensor Networks. Its contributions can be arranged in two main lines.
Firstly, we focus on the efficient management of energy resources in WSNs equipped with finite-size batteries and energy-harvesting devices. To that end, we propose a censoring scheme by which the nodes are able to decide if a message transmission is worthy or not given their energetic condition. In order to do so, we model the system using a Markov Decision Process and use this model to derive optimal policies.
Later, these policies are analyzed in simplified scenarios in order to get insights of their features. Finally, using Stochastic Approximation, we develop low-complexity censoring algorithms that approximate the optimal policy, with less computational complexity and faster convergence speed than other approaches such as Q-learning.
Secondly, we propose a novel diffusion scheme for adaptive distributed estimation in WSNs. This strategy, which we call Decoupled Adapt-then-Combine (D-ATC), is based on keeping an estimate that each node adapts using purely local information and then combines with the diffused estimations by other nodes in its neighborhood.
Our strategy, which is specially suitable for heterogeneous networks, is theoretically analyzed using two different techniques: the classical procedure for transient analysis of adaptive systems and the energy conservation method. Later, as using different combination rules in the transient and steady-state regime is needed to obtain the best performance, we propose two adaptive rules to learn the combination coefficients that are useful for our diffusion strategy. Several experiments simulating both stationary estimation and tracking problems show that our method outperforms state-of-the-art techniques in relevant scenarios. Some of these simulations reveal the robustness of our scheme under node failures.
Finally, we show that both approaches can be combined in a common setup: a
WSN composed of harvesting nodes aiming to solve an adaptive distributed estimation problem. As a result, a censoring scheme is added on top of D-ATC. We show how our censoring approach helps to improve both steady-state and convergence performance of the diffusion scheme.La presente tesis se centra en el desarrollo de algoritmos adaptativos energéticamente eficientes para redes de sensores inalámbricos. Sus contribuciones se pueden englobar en dos líneas principales.
Por un lado, estudiamos el problema de la gestión eficiente de recursos energéticos en redes de sensores equipadas con dispositivos de captación de energía y baterías finitas. Para ello, proponemos un esquema de censura mediante el cual, en un momento dado, un nodo es capaz de decidir si la transmisión de un mensaje merece
la pena en las condiciones energéticas actuales. El sistema se modela mediante un
Proceso de Decisión de Markov (Markov Decision Process, MDP) de horizonte infinito y dicho modelo nos sirve para derivar políticas óptimas de censura bajo ciertos supuestos. Después, analizamos estas políticas óptimas en escenarios simplificados para extraer intuiciones sobre las mismas. Por último, mediante técnicas de Aproximación Estocástica, desarrollamos algoritmos de censura de menor complejidad que aproximan estas políticas óptimas. Las numerosas simulaciones realizadas muestran que estas aproximaciones son competitivas, obteniendo una mayor tasa de convergencia y mejores prestaciones que otras técnicas del estado del arte como las basadas en Q-learning.
Por otro lado, proponemos un nuevo esquema de difusión para estimación distribuida adaptativa. Esta estrategia, que denominamos Decoupled Adapt-then-Combine
(D-ATC), se basa en mantener una estimación que cada nodo adapta con información puramente local y que posteriormente combina con las estimaciones difundidas por los demás nodos de la vecindad. Analizamos teóricamente nuestra estrategia, que es especialmente útil en redes heterogéneas, usando dos métodos diferentes: el método clásico para el análisis de régimen transitorio en sistemas adaptativos y el método de conservación de la energía. Posteriormente, y dado que para obtener el mejor rendimiento es necesario utilizar reglas de combinación diferentes en el transitorio y
en régimen permanente, proponemos dos reglas adaptativas para el aprendizaje de los pesos de combinación para nuestra estrategia de difusión. La primera de ellas está basada en una aproximación de mínimos cuadrados (least-squares, LS); mientras que
la segunda se basa en el algoritmo de proyecciones afines (Afifne Projection Algorithm, APA). Se han realizado numerosos experimentos tanto en escenarios estacionarios como de seguimiento que muestran cómo nuestra estrategia supera en prestaciones a otras aproximaciones del estado del arte. Algunas de estas simulaciones revelan además la robustez de nuestra estrategia ante errores en los nodos de la red.
Por último, mostramos que estas dos aproximaciones son complementarias y las combinamos en mismo escenario: una red de sensores inalámbricos compuesta de nodos equipados con dispositivos de captación energética cuyo objetivo es resolver de manera distribuida y adaptativa un problema de estimación. Para ello, añadimos la capacidad de censurar mensajes a nuestro esquema D-ATC. Nuestras simulaciones muestran que la censura puede ser beneficiosa para mejorar tanto el rendimiento en régimen permanente como la tasa de convergencia en escenarios relevantes de estimación basada en difusión.This work was partially supported by the "Formación de Profesorado Universitario" fellowship from the Spanish Ministry of Education (FPU AP2010-5225).Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Santiago Zazo Bello.- Secretario: Miguel Lázaro Gredilla.- Vocal: Alexander Bertran
A Sparsity-Aware Adaptive Algorithm for Distributed Learning
In this paper, a sparsity-aware adaptive algorithm for distributed learning
in diffusion networks is developed. The algorithm follows the set-theoretic
estimation rationale. At each time instance and at each node of the network, a
closed convex set, known as property set, is constructed based on the received
measurements; this defines the region in which the solution is searched for. In
this paper, the property sets take the form of hyperslabs. The goal is to find
a point that belongs to the intersection of these hyperslabs. To this end,
sparsity encouraging variable metric projections onto the hyperslabs have been
adopted. Moreover, sparsity is also imposed by employing variable metric
projections onto weighted balls. A combine adapt cooperation strategy
is adopted. Under some mild assumptions, the scheme enjoys monotonicity,
asymptotic optimality and strong convergence to a point that lies in the
consensus subspace. Finally, numerical examples verify the validity of the
proposed scheme, compared to other algorithms, which have been developed in the
context of sparse adaptive learning
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