Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Matrix computations (3rd ed.)
Lifting Markov chains to speed up mixing
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Applied Numerical Methods for Engineers Using MATLAB
Applied Numerical Methods for Engineers Using MATLAB
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Convex Optimization
Polynomial filtering in latent semantic indexing for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Decentralized compression and predistribution via randomized gossiping
Proceedings of the 5th international conference on Information processing in sensor networks
A space-time diffusion scheme for peer-to-peer least-squares estimation
Proceedings of the 5th international conference on Information processing in sensor networks
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Differential nested lattice encoding for consensus problems
Proceedings of the 6th international conference on Information processing in sensor networks
Distributed consensus and linear functional calculation in networks: an observability perspective
Proceedings of the 6th international conference on Information processing in sensor networks
Asynchronous distributed averaging on communication networks
IEEE/ACM Transactions on Networking (TON)
Location-aided fast distributed consensus in wireless networks
IEEE Transactions on Information Theory
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
Geographic Gossip: Efficient Averaging for Sensor Networks
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
The capacity of wireless networks
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Fast distributed average consensus algorithms based on advection-diffusion processes
IEEE Transactions on Signal Processing
Distributed consensus with quantized data via sequence averaging
IEEE Transactions on Signal Processing
Adaptive filter algorithms for accelerated discrete-time consensus
IEEE Transactions on Signal Processing
Optimization and analysis of distributed averaging with memory
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Optimization and analysis of distributed averaging with short node memory
IEEE Transactions on Signal Processing
Low-power distributed Kalman filter for wireless sensor networks
EURASIP Journal on Embedded Systems
Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Synchronization of multi-agent systems with delayed control input information from neighbors
Automatica (Journal of IFAC)
Fast decentralized averaging via multi-scale gossip
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Hi-index | 35.70 |
In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. The convergence rate of such distributed algorithms typically depends on the network topology and the weights given to the edges between neighboring sensors, as described by the network matrix. In this paper, we propose to accelerate the convergence rate for given network matrices by the use of polynomial filtering algorithms. The main idea of the proposed methodology is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the convergence rate. Such an algorithm is equivalent to periodic updates in each of the sensors by aggregating a few of its previous estimates. We formulate the computation of the coefficients of the optimal polynomial as a semidefinite program that can be efficiently and globally solved for both static and dynalnic network topologies. We finally provide simulation results that demonstrate the effectiveness of the proposed solutions in accelerating the convergence of distributed consensus averaging problems.