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Localization Algorithm Research On Wireless Sensor Network

Posted on:2017-01-08Degree:MasterType:Thesis
Country:ChinaCandidate:C P LiuFull Text:PDF
GTID:2308330488965450Subject:Computer Science and Technology
Abstract/Summary:PDF Full Text Request
Wireless sensor network consists of a large number of sensor nodes deployed in monitored area through the ways of self-organization and multi-hop. It is widely used in events monitoring such as environmental monitoring, battlefield reconnaissance and other fields. The location of the event is essential to further research. Thus, precise localization is the key to wireless sensor network applications.On the basis of the research achievements on localization algorithms of wireless sensor network, the localization algorithms based on distance have been studied. Localization equations based on distance generally have non-linear characteristics. There are two main methods to solve the nonlinear equations, one uses the Taylor series expansion method to linearize them, the other converts the localization issues to optimization problems.According to the linearization method, a hybrid localization algorithm based on maximum likelihood estimation is proposed. The algorithm firstly utilizes the maximum likelihood estimation method to get the initial values of unknown nodes and provides the initial values to the Taylor series multi-variable expansion localization model, and then utilizes the least squares method to estimate the positions of unknown nodes.Based on the optimization method, two new localization algorithms are proposed. One is a hybrid localization algorithm based on particle swarm optimization, the second is a hybrid localization algorithm based on differential evolution. Both algorithms firstly utilize intelligent optimization algorithms to get the initial values of unknown nodes and provide the initial values to the Taylor series multi-variable expansion localization model, and then utilize the least squares method to estimate the positions of unknown nodes.Simulation results show that the proposed localization algorithms can reduce localization errors and improve localization accuracy effectively.
Keywords/Search Tags:Localization algorithm, Taylor series multi-variable expansion, maximum likelihood estimation, particle swarm optimization, differential evolution
PDF Full Text Request
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