Data Classification Model Of Crowd Sensing Based On Capsule Network

In recent years,crowd sensing has become a cutting-edge research problem in the field of computing.crowd sensing collects perceptual data through the smart phones carried by participants and the unique computing and perception capabilities of smart phones.It is applied to the surrounding environment monitoring,road condition monitoring and intelligent transportation and other related fields.Through the mobile devices carried by mobile users,the perception data of a wide range of sensing tasks can be collected.Compared with other technologies,the sensing tasks can be completed with high efficiency.Participants complete perception tasks at the same time,the sense of mission of the upload issued and perception of information in the crowd sensing network is widely research and attention,the classification of the perception data can be more accurate to provide data to the data of demanders is particularly important,because the basic no sensory data classification in the current study,it will not be able to more accurate to provide corresponding data to data demanders,the categorization of perception data requires researchers to further study.The classification of perception data has been widely studied and concerned.In this paper,we propose a classification model of crowd sensing perception data based on capsule network.Firstly,the status quo of crowd sensing at home and abroad is analyzed,and the problems,main characteristics and data classification of crowd sensing are introduced in detail.Group of crowd sensing of crowd sensing system model platform,mobile users and data classification model,the sense of mission,data requesters and crowd sensing tasks are described in detail,the definition of crowd sensing system model is established to lay theoretical basis,then,crowd sensing of platform perception data type structures,capsule network structure,the constructed model of perception data classification of sample data,the introduction of the sensory data classification model based on capsule network to study the features of sample data,and combining with dynamic routing algorithm and the loss function convolution layer-primary capsule-a digital capsule parameters optimization.Finally,the experimental environment,the structure of capsule network and the visualization of parameter transmission are described,and the model parameter analysis,model optimizer analysis,model accuracy and error analysis are carried out for the data classification model based on capsule network.The simulation experiment shows that compared with the residual network based crowd sensing data classification model,the classification accuracy of the perception data is higher and it can provide data for the requester more accurately.