In this article, a stochastic incrementalsubgradient algorithm for the minimization of a sum of convex functions is introduced. The method sequentially uses partial subgradient information, and the sequence of partia...
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In this article, a stochastic incrementalsubgradient algorithm for the minimization of a sum of convex functions is introduced. The method sequentially uses partial subgradient information, and the sequence of partial subgradients is determined by a general Markov chain. This makes it suitable to be used in networks, where the path of information flow is stochastically selected. We prove convergence of the algorithm to a weighted objective function, where the weights are given by the Cesaro limiting probability distribution of the Markov chain. Unlike previous works in the literature, the Cesaro limiting distribution is general (not necessarily uniform), allowing for general weighted objective functions and flexibility in the method.
In this paper, we propose a modified incrementalsubgradient (MIG) algorithm with a variable step size for positioning and tracking a target in wireless sensor networks. The proposed positioning scheme formulates loca...
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ISBN:
(纸本)9781424426430
In this paper, we propose a modified incrementalsubgradient (MIG) algorithm with a variable step size for positioning and tracking a target in wireless sensor networks. The proposed positioning scheme formulates location estimation as a nonlinear least-squares problem using the received signal strength, and then applies the MIG algorithm with a fixed step size to solve the problem. This scheme can be realized in an iterative, decentralized manner to improve both bandwidth and energy efficiencies. To track a moving target, we further present a step-size adjustment mechanism based on the velocity of the target. In addition, a convergence analysis is given for the MIG-based positioning process. As compared with related positioning and tracking methods, the proposed scheme has better location accuracy and tracking performance.
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