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In-Network Local Distributed Estimation for Power-Constrained Wireless Sensor Networks In-Network Local Distributed Estimation for Power-Constrained Wireless Sensor Networks

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Date added: 10/26/2012
Date modified: 12/28/2013
Filesize: Unknown
Downloads: 1059

 

In this paper, we consider the problem of power-efficient distributed estimation of a localized event in the large-scale Wireless Sensor Networks (WSNs). In order to increase the power efficiency in these networks, we develop a joint optimization problem that involves both selecting a subset of active sensors and the routing structure so that the quality of estimation at a given querying node is the best possible subject to a total imposed communication cost. We first formulate our problem as an optimization problem and show that it is NP-Hard. Then, we propose a local distributed optimization algorithm that is based on an Estimate-and-Forward (EF) strategy, which allows to perform sequentially this joint optimization in an efficient way. We also provide a lower bound for our optimization problem and show that our local distributed optimization algorithm provides a performance that is close to this bound. Although there is no guarantee that the gap between this lower bound and the optimal solution of the main problem is always small, our numerical experiments support that this gap is actually very small in many cases. An important result from our work is that because of the interplay between the communication cost over the links and the gains in estimation accuracy obtained by choosing certain sensors, the traditional Shortest Path Tree (SPT) routing structure, widely used in practice, is no longer optimal, that is, our routing structures provide a better trade-off between the overall power efficiency and the final estimation accuracy obtained at the querying node. Our experimental results show that our algorithms yield a significant energy saving.

On Some Extensions of Fast-Lipschitz Optimization for Convex and Non-convex Problems On Some Extensions of Fast-Lipschitz Optimization for Convex and Non-convex Problems

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Date added: 08/15/2012
Date modified: 02/06/2013
Filesize: 235.03 kB
Downloads: 1490

 

 

Fast-Lipschitz optimization has been recently proposed as a new framework with numerous computational advantages for both centralized and decentralized convex and non-convex optimization problems. Such a framework generalizes the interference function optimization, which plays an essential role distributed radio power optimization over wireless networks. The characteristics of Fast-Lipschitz methods are low computational and coordination complexity compared to Lagrangian methods, with substantial benefits particularly for distributed optimization. These special properties of Fast-Lipschitz optimization can be ensured through qualifying conditions, which allow the Lagrange multipliers to be bound away from zero. In this paper, the Fast-Lipschitz optimization is substantially extended by establishing new qualifying conditions. The results are a generalization of the old qualifying conditions and a relaxation of the assumptions on problem structure so that the optimization framework can be applied to many more problems than previously possible. The new results are illustrated by a non-convex optimization problem, and by a radio power optimization problem which cannot be handled by the existing Fast-Lipschitz theory.

On Rate Allocation for Multiple Plants in a Networked Control System On Rate Allocation for Multiple Plants in a Networked Control System

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Date added: 08/15/2012
Date modified: 12/28/2013
Filesize: 313.41 kB
Downloads: 1416

The problem of allocating communication resources to multiple plants in a networked control system is investigated. In the presence of a shared communication medium, a total  transmission rate constraint is imposed. For the purpose of optimizing the rate allocation to the plants over a finite horizon, two objective functions are considered. The first one is a single-objective function, and the second one is a multi-objective function. Because of the difficulty to derive the closed-form expression of these functions, which depend on the instantaneous communication rate, an approximation is proposed by using high-rate quantization theory. It is shown that the approximate objective functions are convex in the region of interest both in the scalar case and in the multiobjective case. This allows to establish a linear control policy given by the classical linear quadratic Gaussian theory as function of the channel. Based on this result, a new complex relation between the control performance and the channel error probability is characterized.

 

Distributed Estimation Distributed Estimation

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Date added: 08/15/2012
Date modified: 02/06/2013
Filesize: 535.56 kB
Downloads: 1391

 

Distributed estimation plays an essential role in many networked applications, such as communication, networked control, monitoring and surveillance. Motivated by this, the chapter provides an overview on some of the fundamental aspects of distributed estimation over networks. A phenomenon being observed by a number of sensors in networks having a star and a general topology are considered. Under the assumptions of noises and linear measurements, the resulting distributed estimators are derived respectively. The limited bandwidth, communication range and message loss in the communication are considered. Distributed estimators can provide accurate estimates of the parameters of the phenomenon, while the less the limitations are in networks, the lower complexity of the estimator is.

Performance Analysis and Optimization of the Joining Protocol for a Platoon of Vehicles Performance Analysis and Optimization of the Joining Protocol for a Platoon of Vehicles

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Date added: 08/15/2012
Date modified: 12/28/2013
Filesize: Unknown
Downloads: 845

 

Platooning of vehicles allows to saving energy and increasing safety provided that there are reliable wireless communication protocols. In this paper, the optimization of the medium access control (MAC) protocol based on IEEE 802.11e for the platoon joining is investigated. The exchange of prosperous dynamic information among vehicules through certain bounded and closed-fitting timeout windows is challenging. On the one side, safe and secure joining of vehicles to a platoon is time-consuming and in the actual speed of the vehicles may be very difficult. On the other side, the decrease in joining timeout windows results in rising of joining failure. The analytical characterization of the appropriate timeout windows, which is dependent on the rate of exchange messages to request and verify joining, is proposed. By using such a new characterization, the estimation of closed-fitting timeout windows for joining is achieved based on the rate of transferred joining messages. Numerical results show that regular joining timeout windows suffer unacceptable delay for platooning. By contrast, adaptive optimized timeout windows reduce delay of communication. It is concluded that the new optimization proposed in this paper can potentially reduce energy consumption of vehicles and increase safety.

Dictionary Based Reconstruction and Classification of Randomly Sampled Sensor Network Data Dictionary Based Reconstruction and Classification of Randomly Sampled Sensor Network Data

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Date added: 03/14/2012
Date modified: 08/03/2013
Filesize: 209.2 kB
Downloads: 1164

 

In this paper, we propose a method for recovering and classifying WSN data while minimizing the number of samples that need to be acquired, processed, and transmitted. The problem is formulated according to the recently proposed framework of Matrix Completion (MC), which asserts that a low rank matrix can be recovered from a small number of randomly sampled entries. The application of MC in WSN data is motivated by the assumption that sensory data exhibit intrasensor correlations and that these data can be represented using known examples. We formulate the problem as that of recovering the low rank measurement matrix by encoding the contributions of known examples, the dictionary elements, for reconstructing and classifying the data. Experimental results using artificial data suggest that the proposed scheme is able to accurately reconstruct and classify the sensory data from a small number of measurements.