Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
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
Distributed Detection in Sensor Networks With Packet Losses and Finite Capacity Links
IEEE Transactions on Signal Processing
Energy-based sensor network source localization via projection onto convex sets
IEEE Transactions on Signal Processing
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
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In this paper, we propose a dual-decomposition based distributed optimization algorithm for WSNs. The goal is to optimize a global objective function which is a combination of local objective functions known by the sensors only. A gradient-based algorithm is proposed to find the approximate solution for the dual problem. This proposed algorithm is implemented in distributed way, which means each node in WSNs only needs exchange information with its neighboring nodes. In addition, we investigate convergence properties of the dual problem by analyzing the boundness of dual Lagrangian sequence. Simulation results for parameter estimation problem are presented to show the performance of the proposed method against consensus-based approach.