A Research On Skeleton Extraction Algorithm Of Wireless Sensor Network

In recent years,with the rapid progress of wireless sensor network technology and the strong support of relevant policies,the skeleton extraction algorithm of wireless sensor network has attracted the attention of researchers in related fields.For large-scale static wireless sensor networks,the traditional skeleton extraction algorithm has the following two important drawbacks: easy to be disturbed by boundary noise,not able to reflect all the characteristics of wireless sensor networks on the basis of retaining the important visual skeleton branches.In order to improve the existing skeleton extraction algorithm to obtain more optimized skeleton,through the in-depth study of skeleton extraction algorithm,this paper knows that is conducive to optimize the skeleton extraction algorithm by taking into account both node characteristics and boundary characteristics of wireless sensor networks.This paper proposes a skeleton extraction algorithm for the composite evaluation index on the basis of the two indexes node density in node neighborhood and the ratio of distance between nearest edge to near edge and applies it to practice.The simulation results show that the skeleton extraction algorithm proposed in this paper is very effective.In fact,the core of this research work is to give a composite evaluation index to optimize the existing skeleton extraction algorithm by deeply studying the related evaluation indexes of skeleton extraction algorithm on the basis of considering node characteristics and boundary characteristics.The specific research work is as follows:(1)Based on the study of the related concepts,characteristics and applications of wireless sensor networks,this paper introduces and analyzes the existing skeleton extraction algorithms.(2)This paper focuses on the evaluation index of skeleton extraction algorithm based on node characteristics or boundary characteristics,and proposes a skeleton extraction algorithm on the basis of the above two indexes.(3)In this paper,the improved skeleton extraction algorithm and the traditional skeleton extraction algorithm with single evaluation index were simulated and tested by virtual wireless sensor networks with multiple shape rules.The simulation results verify the effectiveness of the algorithm.Then,the performance of skeleton extraction algorithm is further analyzed.Through the corresponding theoretical analysis of the composite evaluation index proposed in this paper,we can see that the composite evaluation index can take into account both node characteristics and boundary characteristics of wireless sensor networks.Correspondingly,the simulation results also show that the skeleton extraction algorithm based on the composite evaluation index can not only effectively avoid boundary noise,but also reflect all the characteristics of WSN while preserving the important visual skeleton branches.In summary,the skeleton extraction algorithm based on composite evaluation index proposed in this paper has a better effect,and has a certain value for application and further research.