143,308 research outputs found
Intelligent Sensor Networks
In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts
Distributed Kernel Regression: An Algorithm for Training Collaboratively
This paper addresses the problem of distributed learning under communication
constraints, motivated by distributed signal processing in wireless sensor
networks and data mining with distributed databases. After formalizing a
general model for distributed learning, an algorithm for collaboratively
training regularized kernel least-squares regression estimators is derived.
Noting that the algorithm can be viewed as an application of successive
orthogonal projection algorithms, its convergence properties are investigated
and the statistical behavior of the estimator is discussed in a simplified
theoretical setting.Comment: To be presented at the 2006 IEEE Information Theory Workshop, Punta
del Este, Uruguay, March 13-17, 200
Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections
Wireless sensor networks (WSNs) have attracted considerable attention in
recent years and motivate a host of new challenges for distributed signal
processing. The problem of distributed or decentralized estimation has often
been considered in the context of parametric models. However, the success of
parametric methods is limited by the appropriateness of the strong statistical
assumptions made by the models. In this paper, a more flexible nonparametric
model for distributed regression is considered that is applicable in a variety
of WSN applications including field estimation. Here, starting with the
standard regularized kernel least-squares estimator, a message-passing
algorithm for distributed estimation in WSNs is derived. The algorithm can be
viewed as an instantiation of the successive orthogonal projection (SOP)
algorithm. Various practical aspects of the algorithm are discussed and several
numerical simulations validate the potential of the approach.Comment: To appear in the Proceedings of the SPIE Conference on Advanced
Signal Processing Algorithms, Architectures and Implementations XV, San
Diego, CA, July 31 - August 4, 200
Signal processing for distributed nodes in smart networks
With increasing environmental concern for energy conservation and mitigating climate change, next generation smart networks are bound to provide improved performance in terms of security, reliability, and energy efficiency. For instance, future smart networks will work in highly complex and dynamic environments and will have distributed nodes that need to interact with each other and may also interact with an energy provider in order to improve their performance. In this context, advanced signal processing tools such as game theory and distributed transmit beamforming can yield tremendous performance gains in terms of energy efficiency for demand management and signal trans-mission in smart networks.
The central theme of this dissertation is the modeling of energy usage behavior of self-seeking distributed nodes in smart networks. The thesis mainly looks into two key areas of smart networks: 1) smart grid networks and 2) wireless sensor networks, and contains: an analytical framework of the economics of electric vehicle charging in smart grids in an energy constrained environment; a study of a consumer-centric energy management scheme for encouraging the consumers in a smart grid to voluntarily take part in energy management; an outage management scheme for efficiently curtailing energy from the consumers in smart grids in the event of a power outage; a comprehensive study of power control of sensors in a wireless sensor network using game theory and distributed transmit beamforming; and finally, an energy aware distributed transmit beamfoming technique for long distance signal transmission in a wireless sensor network.
This thesis addresses the challenges of modeling the energy usage behavior of distributed nodes through studying the propriety of energy users in smart networks, 1) by capturing the interactions between the energy users and energy provider in smart grids using non-cooperative Stackelberg and generalized Nash games, and showing that the socially optimal energy management for users can be achieved at the solution of the games, and 2) by studying the power control of sensors in wireless sensor networks, using a non-cooperative Nash game and distributed transmit beamforming that demonstrates significant transmit energy savings for the sensors. To foster energy efficient transmission, the thesis also studies a distributed transmit beamforming technique that does not require any channel state information for long distance signal transmission in sensor networks.
The contributions of this dissertation are enhanced by proposing suitable system models and appropriate signal processing techniques. These models and techniques can capture the different cost-benefit tradeoffs that exist in these networks. All the proposed schemes in this dissertation are shown to have significant performance improvement when compared with existing solutions. The work in this thesis demonstrates that modeling power usage behavior of distributed nodes in smart networks is both possible and beneficial for increasing the energy efficiency of these networks
Self organizing distributed state estimators
Distributed solutions for signal processing techniques are important for establishing large-scale monitoring and control applications. They enable the deployment of scalable sensor networks for particular application areas. Typically, such networks consists of a large number of vulnerable components connected via unreliable communication links and are sometimes deployed in harsh environment. Therefore, dependability of sensor network is a challenging problem. An efficient and cost effective answer to this challenge is provided by employing runtime reconfiguration techniques that assure the integrity of the desired signal processing functionalities. Runtime reconfigurability has thorough impact both on system design, implementation, testing/validation and deployment. The presented research focuses on the widespreaded signal processing method known as state estimation with Kalman filtering in particular. To that extent, a number of distributed state estimation solutions that are suitable for networked systems in general are overviewed, after which robustness of the system is improved according to various runtime reconfiguration techniques
A Distributed Tracking Algorithm for Reconstruction of Graph Signals
The rapid development of signal processing on graphs provides a new
perspective for processing large-scale data associated with irregular domains.
In many practical applications, it is necessary to handle massive data sets
through complex networks, in which most nodes have limited computing power.
Designing efficient distributed algorithms is critical for this task. This
paper focuses on the distributed reconstruction of a time-varying bandlimited
graph signal based on observations sampled at a subset of selected nodes. A
distributed least square reconstruction (DLSR) algorithm is proposed to recover
the unknown signal iteratively, by allowing neighboring nodes to communicate
with one another and make fast updates. DLSR uses a decay scheme to annihilate
the out-of-band energy occurring in the reconstruction process, which is
inevitably caused by the transmission delay in distributed systems. Proof of
convergence and error bounds for DLSR are provided in this paper, suggesting
that the algorithm is able to track time-varying graph signals and perfectly
reconstruct time-invariant signals. The DLSR algorithm is numerically
experimented with synthetic data and real-world sensor network data, which
verifies its ability in tracking slowly time-varying graph signals.Comment: 30 pages, 9 figures, 2 tables, journal pape
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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