Research On Task Assignment Mechanism In Mobile Crowdsensing Network

Mobile crowdsensing refers to users carrying smart devices cooperate to accomplish a complex task,which is difficult for an individual to handle in a specific sensing area.In recent years,smart devices(mobile phones,tablets,vehicle terminals,etc.)have integrated more and more sensors,with powerful performances of storage,computing,sensing and processing,enabling users to perceive the surrounding information at any time through these devices.Different from the traditional static sensor network,mobile crowdsensing regards the user who carries the smart device as a dynamic sensor node,which has the advantages of wide sensing range,large sensing scale,low maintenance cost,etc.,having broad prospects and application space in environmental detection,intelligent transportation,indoor positioning and smart cities.Among them,the task assignment mechanism is a key issue in the research of mobile crowdsensing.The core of task assignment is the user who carries the smart device.In the actual perception scenario,due to the irregularity of user motion,the existing task assignment strategy is not reasonable.In the face of complex and varied task requirements,the applicability of the algorithm is not high.Therefore,this thesis studies the task assignment mechanism in mobile crowdsensing network,and the specific research contents and results are as follows:In the task assigner's assignment model while moving,the assigner assigns tasks to the user he meets in the mobile process,and the user returns the perceived result through the encounter with the assigner again after completing the tasks.The existing algorithm utilizes the user's encountering rules to formulate the assignment strategy,and the user participates in the sensing process voluntarily.In some scenarios,the user may be assigned too many or too few tasks,and the practicality is lacking.Therefore,based on the existing algorithms,this thesis designs a Reward and Multi-stage Crowdsensing Task Assignment Framework(RM-CSTA)with the goal of minimizing the average completion time of the task.The task assigner and the user achieve equilibrium through the Stackleberg process: the assigner minimizes the average completion time of the task,and the user maximizes the rewards.Among them,the Butterworth low-pass filter curve is used as the reward function of the game,and the less time to complete the task,the more rewards are obtained.The user can use the redistribution strategy to reduce the time for completing the task to improve his income.Then the task assignment algorithm(Multi-stage Online Task Assignment(MOTA)of the framework is given and the algorithm is analyzed.The simulation experiments in the real scenario and the simulation scenario prove that the task completion time of the MOTA algorithm can be reduced by an average of 10%-15%.(2)The optimized algorithm can be applied to the actual scenario and the multi-stage assignment strategy of the algorithm allows the user to assign tasks to more users,thereby improving the applicability of the algorithm.However,in some scenarios,the performance of the MOTA algorithm will decrease due to the limited capacity of the participating users or the failure to meet other users.Therefore,this thesis innovatively proposes a social-based task assignment algorithm to optimize the multi-stage assignment strategy of MOTA algorithm,combines the user's social relationship network and the user's ability threshold.In the process of crowdsensing,users can actively optimize the assigned subset of tasks according to their own capabilities,which can improve user participation and ensure the perceived data quality to a certain extent.Theoretical analysis proves that the Social-MOTA algorithm is more suitable for task assignment in general perception scenarios with large scope and complex tasks.The simulation results show that the task completion time of the Social-MOTA algorithm is reduced by about 15% compared with the MOTA algorithm.