Research On Task Assignment And Incentive Mechanism In Mobile Crowd Sensing

Mobile crowd sensing has the characteristics of low cost,wide coverage and simple maintenance.Unlike traditional static sensor networks,mobile crowd sensing uses the mobile device carried by the user as the sensing unit to realize the sensing task distribution and collection of perception data.At present,in the fields of environment and traffic condition monitoring,mobile crowd sensing has a wide range of application prospects.In mobile crowd sensing,task assignment strategies and user incentive mechanisms are two key research issues.In the existing research,there are few researches on task assignment strategies with wide temporal and spatial spans such as noise monitoring.The user fatigue caused by large temporal and spatial span scenarios is very different,and the problem of temporal and spatial complexity has not been effectively solved.At the same time,under the premise of limited budget,the use of auctions and other compensation methods for incentive mechanism design has the problems that some difficult tasks cannot be effectively tendered and the quality of task execution cannot be guaranteed.Aiming at the characteristics of wide temporal and spatial coverage of urban noise monitoring,large differences in user fatigue in different periods,and low data collection quality,this paper designs a mobile crowd sensing task assignment strategy based on space-time joint optimization.Aiming at the situation of low user enthusiasm for participation in mobile crowd sensing,unclaimed difficult tasks,and unguaranteed task completion quality,a dual-process incentive mechanism for mobile crowd sensing based on the combination of engineering bidding and auction is designed.(1)Task assignment strategy for mobile crowd sensing based on space-time joint optimization.The task assignment efficiency for noise monitoring scenarios with a large time span is low.A large space span will increase the complexity of task assignment,and a large time span will lead to a large difference in the fatigue of participating users and affect the effect of task execution.This strategy uses the collective coverage idea to model large-scale spatio-temporal task assignment problems such as urban noise monitoring,comprehensively considers the differences in user fatigue under the largescale spatio-temporal span and the historical task completion of task participants,and takes the task time-space constraints as constraints to construct,the task assignment cost performance index is taken as the optimization target,and the heuristic genetic algorithm is used to solve the problem.(2)Dual-process mobile crowd sensing incentive mechanism based on engineering bidding and auction models.Existing reward-based incentive mechanisms do not consider perceiving the user's task execution ability,and the user's enthusiasm for participating in difficult tasks is not high.The dual-process incentive mechanism mentioned in this article includes the overall task bidding link and the remaining difficult task auction link according to time.The overall task bidding link combines the user's bidding quotation and perception ability for comprehensive evaluation,constructs platform benefit indicators,and selects the user with the highest platform benefit score to complete the task bidding.In response to the remaining difficult tasks of unmanned bidding,a method to improve budget utilization was designed,and an improved Dutch auction mechanism was introduced to further enhance the enthusiasm of users to participate and the success rate of overall incentives.Based on the MATLAB platform,this paper tests the performance of the abovementioned task assignment strategy and incentive mechanism and compares and analyzes them with related algorithms.The results show that the task assignment algorithm proposed in this paper shows good performance in terms of task completion quality,cost performance index,and task execution redundancy,with an average performance increase of 26.5%.The proposed dual-process incentive mechanism is effective in budget utilization,total platform benefits,and incentive success.Compared with similar algorithms,the performance is better in terms of rate,with an average performance increase of 17.6%.