A stochastic multiobjective optimization framework for wireless sensor networks

  • Authors:
  • Shibo He;Jiming Chen;Weiqiang Xu;Youxian Sun;Preetha Thulasiraman;Xuemin Shen

  • Affiliations:
  • State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China;State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China and Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada;State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China;State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, China;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada

  • Venue:
  • EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
  • Year:
  • 2010

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Abstract

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 timevarying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.