Research On Stable Task Allocation Mechanism And User Incentive Mechanism In Mobile Crowdsensing

Mobile Crowdsensing(MCS)has the characteristics of strong flexibility,low cost,and wide coverage.Unlike traditional static sensor networks,it can use a large number of mobile terminal devices such as smartphones owned by ordinary users as basic sensing units to distribute sensing tasks and collect sensing data.At present,mobile crowdsensing has broad application prospects in fields such as environmental and traffic monitoring.In mobile crowdsensing systems,reasonable task allocation mechanisms and user motivation mechanisms are two important research topics.In existing work,there is relatively little research on task allocation mechanisms that have a significant impact on the allocation results due to subjective preferences of users such as shuttle transportation.If there is a pair of tasks and users who prefer each other but do not match each other,users may have motivation to deviate from their perceived tasks.Therefore,the problem of unstable allocation results caused by neglecting user preference matching urgently needs to be addressed.At the same time,under the premise of limited budget,the design of user incentive mechanisms using auction methods often focuses on single objective optimization.However,in real-world mobile crowdsensing applications,such as road traffic monitoring,which requires consideration of multiple factors such as coverage,timeliness,and regionality,there are usually multiple objectives that need to be optimized.Therefore,the paper focuses on the stable task allocation mechanism and multi-objective optimization user incentive mechanism in mobile crowdsensing.The main work and research results of the paper are as follows:(1)Research on stable task allocation mechanism based on user preference matching.In perception task allocation,where subjective preferences of users such as shuttle buses have a significant impact on the allocation results,ignoring user preference matching leads to unstable task allocation results,this paper studies a stable task allocation mechanism based on user preference matching.In a mobile crowdsensing system with unified service quality,a preference list is generated for mobile users and perception tasks.Considering the budget and quality constraints of perception tasks,a multi to one task stable allocation model is constructed with the optimization goal of maximizing the number of matches,and a stable allocation algorithm is proposed for solution.The simulation experiment results show that the allocation algorithm has an average task success rate of 8.04% higher than the benchmark algorithm,and 10.5% higher satisfaction for mobile users and perceived tasks.(2)Research on stable task allocation mechanism based on advanced snake shaped optimization algorithm.Based on the study of preference matching between mobile users and perceived tasks,this paper investigates the stable task allocation mechanism for heterogeneous users in real-world mobile crowdsensing applications,where users have different levels of service quality for different perceived tasks.Considering the heterogeneity of mobile users and the preferences of users and tasks under perceived task quality and budget constraints,a multi to one task allocation model is constructed with the optimization goal of minimizing unsatisfactory matching pairs.Due to the NP hard nature of this problem,advanced snake shaped optimization algorithms are used to solve it.The simulation experiment results demonstrate that the task allocation mechanism based on advanced snake optimization algorithm has an average improvement of 9.76% in overall user satisfaction compared to other algorithms,and has better performance in terms of runtime.(3)Research on multi-objective reverse auction incentive mechanism with budget constraints.In mobile crowdsensing applications,user incentive mechanisms often have multiple objectives that need to be optimized under budget constraints of perception tasks.This paper studies a multi-objective reverse auction incentive mechanism with budget constraints.Optimize the total value function,coverage function,and user reputation function with budget constraints through problem transformation,and solve multi-objective optimization problems by combining binary search and greedy heuristic solving.The simulation experiment results show that compared with the greedy method,the user incentive method proposed in the paper improves server utility by 8.03%.