Dual-Decomposition Approach for Distributed Optimization in Wireless Sensor Networks

摘要

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.