690 research outputs found
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
Distributed Consensus over Wireless Sensor Networks Affected by Multipath Fading
The design of sensor networks capable of reaching a consensus on a globally
optimal decision test, without the need for a fusion center, is a problem that
has received considerable attention in the last years. Many consensus
algorithms have been proposed, with convergence conditions depending on the
graph describing the interaction among the nodes. In most works, the graph is
undirected and there are no propagation delays. Only recently, the analysis has
been extended to consensus algorithms incorporating propagation delays. In this
work, we propose a consensus algorithm able to converge to a globally optimal
decision statistic, using a wideband wireless network, governed by a fairly
simple MAC mechanism, where each link is a multipath, frequency-selective,
channel. The main contribution of the paper is to derive necessary and
sufficient conditions on the network topology and sufficient conditions on the
channel transfer functions guaranteeing the exponential convergence of the
consensus algorithm to a globally optimal decision value, for any bounded delay
condition.Comment: Paper submitted to IEEE Transactions on Signal Processing, August
2007. Revised November 30, 2007. Accepted January 14, 200
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The role of smart sensor networks for voltage monitoring in smart grids
The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, distributed voltage monitoring is one of the main issues to address. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables describing the global grid state
Distributed Estimation and Control of Algebraic Connectivity over Random Graphs
In this paper we propose a distributed algorithm for the estimation and
control of the connectivity of ad-hoc networks in the presence of a random
topology. First, given a generic random graph, we introduce a novel stochastic
power iteration method that allows each node to estimate and track the
algebraic connectivity of the underlying expected graph. Using results from
stochastic approximation theory, we prove that the proposed method converges
almost surely (a.s.) to the desired value of connectivity even in the presence
of imperfect communication scenarios. The estimation strategy is then used as a
basic tool to adapt the power transmitted by each node of a wireless network,
in order to maximize the network connectivity in the presence of realistic
Medium Access Control (MAC) protocols or simply to drive the connectivity
toward a desired target value. Numerical results corroborate our theoretical
findings, thus illustrating the main features of the algorithm and its
robustness to fluctuations of the network graph due to the presence of random
link failures.Comment: To appear in IEEE Transactions on Signal Processin
Smart Wireless Sensor Networks
The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks
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
Achieving Distributed Consensus in UWB Sensor Networks: A Low Sampling Rate Scheme with Quantized Measurements
Distributed consensus in sensor networks has received great attention in the last few years. Most of the research activity has been devoted to study the sensor interactions that allow the convergence of distributed consensus algorithms toward a globally optimal decision. On the other hand, the problem of designing an appropriate radio interface enabling such interactions has received little attention in the literature. Motivated by the above consideration, in this work an ultrawideband sensor network is considered and a physical layer scheme is designed, which allows the active sensors to achieve consensus in a distributed manner without the need of any admission protocol. We focus on the class of the so-called quantized distributed consensus algorithms in which the local measurements or current states of each sensor belong to a finite set. Particular attention is devoted to address the practical implementation issues as well as to the development of a receiver architecture with the same performance of existing alternatives based on an all-digital implementation but with a much lower sampling frequency on the order of MHz instead of GHz
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