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WSNs Localization Algorithm Based On Self-Adaptive Penalty Function Optimization Particle Swarm Optimization

Posted on:2020-01-24Degree:MasterType:Thesis
Country:ChinaCandidate:Y B HanFull Text:PDF
GTID:2428330575499053Subject:Control engineering
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Wireless Sensor Networks(WSN)is a distributed sensor network composed of a large number of sensor nodes randomly deployed in the target area with self-organizing and multi-hop communication capabilities.It is widely used in military,intelligent transportation,environmental monitoring,medical and health,intelligent agriculture and other fields.Among many WSN technologies,node location is not only the basis and important component of WSN application,but also one of the core technologies of WSN theoretical research,and it is also the hot spot of domestic and foreign scholars.This paper,firstly introduces the research background,significance and research status of node positioning at home and abroad,and then illustrates the importance of this topic.Aiming at the problems of low precision and slow convergence speed in node location of wireless sensor network,an adaptive penalty function optimization algorithm is proposed based on the traditional particle swarm optimization(PSO)positioning technique.The research and discussion are made from the following two aspects:(1)By using PSO algorithm to improve node positioning accuracy: since the penalty factors of traditional penalty functions are fixed,the selection of penalty factors is difficult to control,and too large or too small will lead to ill-conditioned development of the algorithm.In this paper,an adaptive penalty function is introduced to iteratively optimize the size of the penalty factor according to the proportion of the feasible solution of the particle population.In this way,the convergence speed of the algorithm is accelerated,the positioning accuracy and the stability of the algorithm are improved,and the problem that the algorithm is prone to fall into local optimum is solved.(2)By reducing the ranging error to improve the positioning accuracy: the RSSI ranging results have a great effect on the positioning accuracy of the algorithm.In order to reduce the influence of ranging error on positioning error,this topic improves the positioning accuracy of the algorithm by using the weighted summation coefficient of ranging result and calculation result as the constraint value of the penalty term of the penalty function to optimize the search space of particles.Through the analysis of simulation and experimental results,it can be seen that the WSN positioning algorithm(PSOAPF algorithm)designed in this topic and based on the adaptive penalty function optimization PSO has improved the positioning accuracy and convergence speed compared with the similar optimization algorithm.
Keywords/Search Tags:Wireless sensor network, Self-adaptive penalty function, Particle swarm optimization, Node localization, RSSI ranging
PDF Full Text Request
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