Adaptive filter theory
Matrix computations (3rd ed.)
Vector extrapolation methods. applications and numerical comparison
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 vol. II: interpolation and extrapolation
Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Locally constructed algorithms for distributed computations in ad-hoc networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Convex Optimization
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Adaptive Filters
Adaptive Processing over Distributed Networks
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Polynomial filtering for fast convergence in distributed consensus
IEEE Transactions on Signal Processing
Accelerated distributed average consensus via localized node state prediction
IEEE Transactions on Signal Processing
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
IEEE Transactions on Signal Processing - Part II
An efficient robust adaptive filtering algorithm based on parallelsubgradient projection techniques
IEEE Transactions on Signal Processing
The capacity of wireless networks
IEEE Transactions on Information Theory
Distributed computation of averages over ad hoc networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
In many distributed systems, the objective is to reach agreement on values acquired by the nodes in a network. A common approach to solve such problems is the iterative, weighted linear combination of those values to which each node has access. Methods to compute appropriate weights have been extensively studied, but the resulting iterative algorithms still require many iterations to provide a fairly good estimate of the consensus value. In this study we show that a good estimate of the consensus value can be obtained with few iterations of conventional consensus algorithms by filtering the output of each node with set-theoretic adaptive filters. We use the adaptive projected subgradient method to derive a set-theoretic filter requiring only local information available to each node and being robust to topology changes and erroneous information about the network. Numerical simulations show the good performance of the proposed method.