Matrix analysis
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Convex Optimization
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
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Communication constraints in the average consensus problem
Automatica (Journal of IFAC)
Universal decentralized detection in a bandwidth-constrained sensor network
IEEE Transactions on Signal Processing - Part I
A distributed minimum variance estimator for sensor networks
IEEE Journal on Selected Areas in Communications
Adaptive IEEE 802.15.4 protocol for energy efficient, reliable and timely communications
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Design and implementation of a robust sensor data fusion system for unknown signals
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
SenShare: transforming sensor networks into multi-application sensing infrastructures
EWSN'12 Proceedings of the 9th European conference on Wireless Sensor Networks
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Motivated by a peer-to-peer estimation algorithm in which adaptive weights are optimized to minimize the estimation error variance, we formulate and solve a novel nonconvex Lipschitz optimization problem that guarantees global stability of a large class of peer-to-peer consensus-based algorithms for wireless sensor network. Because of packet losses, the solution of this optimization problem cannot be achieved efficiently with either traditional centralized methods or distributed Lagrangian message passing. We prove that the optimal solution can be obtained by solving a set of nonlinear equations. A fast distributed algorithm, which requires only local computations, is presented for solving these equations. Analysis and computer simulations illustrate the algorithm and its application to various network topologies.