Distributed Algorithms
Load Balancing in Parallel Computers: Theory and Practice
Load Balancing in Parallel Computers: Theory and Practice
Local Divergence of Markov Chains and the Analysis of Iterative Load-Balancing Schemes
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
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
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
IEEE Transactions on Signal Processing - Part II
Incremental Adaptive Strategies Over Distributed Networks
IEEE Transactions on Signal Processing
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
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
IEEE Journal on Selected Areas in Communications
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In the past few years, the problem of distributed parameter estimation has received a lot of attention, particularly for the applications in sensor networks. This paper focuses on the distributed iterative parameter estimation scheme. An alternative form of the consensus averaging-based algorithm is introduced, in which each node iteratively updates its estimate by adding a weighted sum of its own and its neighbors' estimate, with the time-varying weight matrices. To improve the convergence of this distributed iterative scheme, an adaptive weight matrix modification algorithm is proposed, in which each node is modeled as an adaptive filter. The weight matrix is modified using the steepest descent method. With the non-persistent measurement data, a linear predictor is designed to provide the reference signal in the adaptive filter. As a result, the intermediate estimate of each node is driven closer to the steady-state estimate. Simulation results show that the proposed algorithm decreases the intermediate estimation error and accelerates the consensus convergence, with the nearly optimal steady-state estimation performance.