Location Estimation in Wireless Sensor Networks Based on Probabilistic Model with Variant Variance and Evolutionary Algorithm
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    Abstract:

    Location is a crucial part of wireless sensor networks technologies and applications. RSS-based (based on received signal strength) location estimations play an important role in practice. Considering the characteristic that the variance of RSS varies in different estimation points, a practical RSS-based probabilistic model is tailored and established according to the probability-based maximum likelihood in this paper. Next, taking the highly nonlinear characteristic of the object function in this probabilistic model, a location approach using the probability maximum with evolutionary algorithm (PMEA), which corresponds more to the characteristic of communication of the sensors, is proposed to find out the maximum likelihood point. The convergence is proved by the stochastic process. The results of the proposed algorithm, when implemented in a public dataset, show that this proposed probabilistic model and PMEA outperform existing solutions in terms of RSS-based location estimation accuracy.

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叶苗,王宇平.基于变方差概率模型和进化计算的WSN 定位算法.软件学报,2013,24(4):859-872

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History
  • Received:October 11,2011
  • Revised:April 24,2012
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  • Online: March 26,2013
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