78,311 research outputs found
Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation
This paper presents adaptive link selection algorithms for distributed
estimation and considers their application to wireless sensor networks and
smart grids. In particular, exhaustive search--based
least--mean--squares(LMS)/recursive least squares(RLS) link selection
algorithms and sparsity--inspired LMS/RLS link selection algorithms that can
exploit the topology of networks with poor--quality links are considered. The
proposed link selection algorithms are then analyzed in terms of their
stability, steady--state and tracking performance, and computational
complexity. In comparison with existing centralized or distributed estimation
strategies, key features of the proposed algorithms are: 1) more accurate
estimates and faster convergence speed can be obtained; and 2) the network is
equipped with the ability of link selection that can circumvent link failures
and improve the estimation performance. The performance of the proposed
algorithms for distributed estimation is illustrated via simulations in
applications of wireless sensor networks and smart grids.Comment: 14 figure
Distributed Signal Processing Algorithms for Wireless Networks
Distributed signal processing algorithms have become a key approach for statistical inference in wireless networks and applications such as wireless sensor networks and smart grids. It is well known that distributed processing techniques deal with the extraction of information from data collected at nodes that are distributed over a geographic area. In this context, for each specific node, a set of neighbor nodes collect their local information and transmit the estimates to a specific node. Then, each specific node combines the collected information together with its local estimate to generate an improved estimate. In this thesis, novel distributed cooperative algorithms for inference in ad hoc, wireless sensor networks and smart grids are investigated. Low-complexity and effective algorithms to perform statistical inference in a distributed way are devised. A number of innovative approaches for dealing with node failures, compression of data and exchange of information are proposed and summarized as follows: Firstly, distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks are presented. Both incremental and diffusion adaptive solutions are considered. Secondly, adaptive link selection algorithms for distributed estimation and their application to wireless sensor networks and smart grids are proposed. Thirdly, a novel distributed compressed estimation scheme is introduced for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices. Lastly, a novel distributed reduced-rank scheme and adaptive algorithms are proposed for distributed estimation in wireless sensor networks and smart grids. The proposed distributed
scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced–dimension parameter vector
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