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Open Access Open Badges Research Article

A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks

Shibo He1, Jiming Chen12, Weiqiang Xu1, Youxian Sun1, Preetha Thulasiraman2 and Xuemin(Sherman) Shen2*

Author Affiliations

1 State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

2 Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, N2L 3G1

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EURASIP Journal on Wireless Communications and Networking 2010, 2010:430615  doi:10.1155/2010/430615

Published: 20 May 2010


In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.