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In Wireless Sensor Networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, the authors propose a stochastic multi-objective optimization approach to solve such kind of problem. They first formulate a general multi-objective optimization problem. They 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. They show theoretically that their algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming.
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