Research And Implementation Of Task Allocation For Crowd Sensing In Internet Of Vehicles

Today,people are increasingly eager to have a thorough sensing of the physical environment.This is a new opportunity for the rapid development of Internet of Things technology.With the continuous advancement of wireless communications and sensor technologies,wireless mobile terminal devices have exploded to millions of households.Compared with ordinary mobile devices,due to the loading of various complicated sensors,the performance of the car in terms of sensing,calculation,storage and communication is more powerful,mobility is stronger,and the coverage is wider.Crowd sensing in internet of vehicles makes it possible to fully and thoroughly sense the physical environment.It is widely used in environmental monitoring,traffic management,smart cities,public safety,telemedicine,social services,and other areas,becoming a hot topic of concern.Task allocation for crowd sensing in internet of vehicles is mainly responsible for selecting the most appropriate participating vehicle for data sensing.It has an important influence on the comprehensiveness of data collection,the completion rate of tasks,and the quality of data collection,and therefore it has become the focus of current research.In view of the deficiencies in task allocation problems for crowd sensing in internet of vehicles,this paper discusses and studies various schemes.Firstly,the trajectory-based task allocation for crowd sensing in internet of vehicles is studied,and a trajectory-based task allocation scheme is proposed.Based on the study of spatiotemporal availability measurement models and analysis methods,the spatiotemporal availability of participants is measured by using vehicle trajectories.After the trajectory is obtained,the minimal-cover problem in the field of computational geometry is studied to solve the problem of the coverage of the minimum number of participants based on the availability of the trajectory.Based on this,a trajectory-based task allocation problem is proposed.Aiming at the actual situation,a pricing model is defined and a reputation evaluation strategy is proposed.The problem is converted into an integer linear programming optimization problem and a solution is formulated.The use of the minimum number of participating vehicles to meet the required coverage level for the sensing task enables a reasonable and effective task allocation.Secondly,the paper studies and designs the compressive-sensing-based task allocation for crowd sensing in internet of vehicles,and proposes a scheme using compressive sensing technology based on the spatiotemporal correlation between sensing data.Iteratively selects areas for sensing task allocation,deduces the missing data through compressed sensing at each period,and evaluates current data errors until the task quality requirements are met.After the area set is obtained,the integer linear programming optimization problem is also used to select the participating vehicles.Further reduce the task allocation costs,avoid redundant task allocation,and achieve a reasonable allocation of resources.Finally,the realization and verification of the task allocation scheme for crowd sensing in internet of vehicles was implemented.Using the theories and methods detailed in the previous two parts,the real vehicle trajectory data set and air quality index data set were used to verify the above two task allocation schemes on the simulation platform.The experimental results are in-depth analysis and comparison,and achieved obvious results.And for the current few case studies of the crowd sensing in internet of vehicles,there is a certain degree of innovation.