Network lifetime maximization for estimation in multihop wireless sensor networks
IEEE Transactions on Signal Processing
Power-efficient dimensionality reduction for distributed channel-aware kalman tracking using WSNs
IEEE Transactions on Signal Processing
Distributed LMS for consensus-based in-network adaptive processing
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
Quantization, channel compensation, and energy allocation for estimation in wireless sensor networks
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Mean square convergence of consensus algorithms in random WSNs
IEEE Transactions on Signal Processing
Binary consensus over fading channels
IEEE Transactions on Signal Processing
Quantization, channel compensation, and optimal energy allocation for estimation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Decentralized subspace tracking via gossiping
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Foundations and Trends® in Machine Learning
Distributed static linear Gaussian models using consensus
Neural Networks
Optimal decentralized Kalman filter and Lainiotis filter
Digital Signal Processing
| Hi-index | 35.70 |
Distributed algorithms are developed for optimal estimation of stationary random signals and smoothing of (even nonstationary) dynamical processes based on generally correlated observations collected by ad hoc wireless sensor networks (WSNs). Maximum a posteriori (MAP) and linear minimum mean-square error (LMMSE) schemes, well appreciated for centralized estimation, are shown possible to reformulate for distributed operation through the iterative (alternating-direction) method of multipliers. Sensors communicate with single-hop neighbors their individual estimates as well as multipliers measuring how far local estimates are from consensus. When iterations reach consensus, the resultant distributed (D) MAP and LMMSE estimators converge to their centralized counterparts when inter-sensor communication links are ideal. The D-MAP estimators do not require the desired estimator to be expressible in closed form, the D-LMMSE ones are provably robust to communication or quantization noise and both are particularly simple to implement when the data model is linear-Gaussian. For decentralized tracking applications, distributed Kalman filtering and smoothing algorithms are derived for any-time MMSE optimal consensus-based state estimation using WSNs. Analysis and corroborating numerical examples demonstrate the merits of the novel distributed estimators.