329 research outputs found
A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks
Wireless sensor networks consist of sensor nodes that are physically
distributed over different locations. Spatial filtering procedures exploit the
spatial correlation across these sensor signals to fuse them into a filtered
signal satisfying some optimality condition. However, gathering the raw sensor
data in a fusion center to solve the problem in a centralized way would lead to
high energy and communication costs. The distributed adaptive signal fusion
(DASF) framework has been proposed as a generic method to solve these signal
fusion problems in a distributed fashion, which reduces the communication and
energy costs in the network. The DASF framework assumes that there is a common
goal across the nodes, i.e., the optimal filter is shared across the network.
However, many applications require a node-specific objective, while all these
node-specific objectives are still related via a common latent data model. In
this work, we propose the DANSF algorithm which builds upon the DASF framework,
and extends it to allow for node-specific spatial filtering problems.Comment: 5 page
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
Distributed canonical correlation analysis in wireless sensor networks with application to distributed blind source separation
status: publishe
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
Distributed field estimation in wireless sensor networks
This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks
- …