Data Reuse Modeling And Incentive Mechanism Designing For Mobile Crowd Sensing

The rapid development of the world's scientific information technology has promoted the emergence and development of the Internet of Things.The Internet of Things collects various information through sensing devices and forms a giant network with the Internet,Providing the needs of society's production and life.Through the fusion and docking of the Internet and the Internet of things,sensor devices are embedded into every corner of the urban system to produce cooperative effect and improve urban quality.In recent years,the rapid development of sensor technology has made it possible for people to sense larger scale and more complex social tasks.Mobile crowdsensing is born in this context.It is a user-centered sensing model and it's used to accomplish a wide range of task sensing.The emergence of mobile crowdsensing brings more possibilities for social sensing activities,but at the same time there are some problems,such as repeated collection of sensory data,the quality of the collected data is not high enough to meet the requirements and the authenticity of the data,etc.These questions are related to whether the entire sensing task can be successfully completed.Therefore,how to solve the repeated collection of data,ensure the quality and authenticity of the collected data will be the key research of this paper.This paper will mainly studies the mobile crowdsensing network scenario base on data reuse.In the traditional sensing model,different tasks are isolated from each other,which may cause the same data requirements among different tasks and lead to the problem of repeated data collection.We propose a "task/data/user" mobile crowdsensing model,adding a data layer between the task layer and the user layer for data multiplexing.For the problem of low sensory data quality,we consider a quality factor for each sensory data to ensure that the data meets the requirements of the task.Each layer is modeled separately and then we build the entire system model and obtain the optimal task scheduling schema by classical algorithm,and the corresponding results are obtained by setting parameters in different scenarios.Based on the fact that the above-mentioned model may appear the data reported by users or tasks is not true and reliable,this paper further studies the related incentive mechanism.Traditional incentives no longer apply to the data reuse network model,so we will propose a truthful double auction mechanism.We can ensure that the data provided by task publishers and users is truly trustworthy under this mechanism.In addition,in order to avoid the platform losing money,this paper proposes the concept of reserve price to facilitate the balance between platform budget and optimized scheduling.In summary,this paper will do some research on task scheduling and optimization,quality assurance and incentive mechanism,and then get the results after comparing with the existing models.The network model studied in this paper has a high degree of universality and can be applied to other similar sensing scenes and models.It has far-reaching significance for the theoretical research and promotion of mobile crowdsensing.