| With the widespread application of mobile intelligent devices,mobile crowdsensing(MCS)has emerged at the right moment.MCS assigns perceptual tasks to many ordinary users,and uses intelligent devices carried by ordinary users as the perceptual unit to collect and perceive data,thereby completing complex social sensing tasks,and thus has high practicality.Task assignment is an important part of MCS research,and it has received widespread attention from researchers.At present,the diversity oriented MCS task assignment research mainly focused on location diversity,time diversity,and capability diversity,etc.,without considering the needs of perceptual tasks for the diversity of user types,which may lead to a reduction in the quality of perceptual task.For example,in the task of urban noise monitoring,if a single type of intelligent device is used,the error in the perception result is large,and in order to improve the monitoring quality,multiple types of mobile devices are required to participate in the monitoring together.Therefore,this paper conducts an in-depth research on MCS task assignment problem facing the needs of user type diversity,and its main work includes the following two aspects:(1)Aiming at the problem of single task assignment in MCS,which requires diversity of user type,this paper proposes a task assignment method to maximize the user type diversity.For the single task assignment,if the types of users selected are evenly distributed among the types required by the task,then the calculated type diversity value is the largest.The specific method of selecting users is to select users in turn among the types required by the task.Aiming at the single task assignment problem,this paper introduces the concept of information entropy to calculate the user type diversity value.Based on the assumption that each user belongs to only one type,the MCS task assignment problem is formalized with the goal of maximizing the user type diversity,and three heuristic task assignment algorithms are proposed.The experimental simulation results show that,compared with the cost-based greedy algorithm,the greedy algorithm based on cost or unit reward for task assignment based on the required type of task,achieving better type diversity value and total profit.On the basis of the above experiments,the user type changed from following a uniform distribution to following a normal distribution.Simulation results show that the performance of the three algorithms has not changed.(2)Aiming at the multi-task assignment problem in MCS,which requires diversity of user type,this paper proposes a task assignment method based on time constraints to maximize the user type diversity.For the problem of multi-task assignment,since users belong to multiple different types,the selected user types cannot ensure uniform distribution among all types.Therefore,entropy gain is introduced in this paper to select users.The specific method of selecting users is to select the user with the largest entropy gain from the candidate user set for each task in turn.Aiming at the multi-task assignment problem,this paper introduces the concept of information entropy to calculate the user type diversity value and entropy gain.Based on the assumption that each user belongs to multiple types,with the goal of maximizing the user type diversity,the MCS task allocation problem is formalized.Based on this,two heuristic task assignment algorithms are proposed.The experimental simulation results show that compared with the random algorithm,the task assignment method based on information entropy and unit reward achieves better type diversity value and total profit.This paper mainly solves the task assignment for user type diversity in MCS,proposes corresponding algorithm solutions for single task and multi-task assignment,and further verifies the performance of the algorithm through simulation experiments,effectively improving the type diversity value and the total profit,which provides convenience for subsequent research. |
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