Robust Mobile Crowdsensing Incentive Mechanism Design For Spatial Tasks

With the rapid development of sensor-embedded devices,the great potential of mobile crowdsensing has attracted attention from both industries and the research communities.Mobile crowdsensing is usually composed of three parts: a cloud-based platform,some data demanders,and a number of participants(usually mobile phone users).Data demanders publish data demand tasks through the cloud platform,and then mobile phone users use various embedded sensors in their mobile phones to to sense,collect and then submit data.In general,the user needs to move to a specific location to perform the corresponding sensing task,mobile phone users usually consume some resources,such as time,power,storage,and data usage.Especially when the sensing areas of tasks overlap each other,the platform requires users to submit their own locations to distribute tasks.In addition,the location data submitted by users is also related to the quality of the sensing data.Therefore,it is very important to design an effective robust incentive mechanism for spatial tasks.The research work of this article is mainly reflected in the following two aspects:(1)In most existing mechanisms,the special problem of overlap between the sensing area of the task and the active area of the user is not considered.In order to attract users to participate in the mobile crowdsensing for space tasks,we use reverse auction as a technical method to design two incentive mechanisms: one is the incentive mechanism for location coordinate model,and the other is the incentive mechanism for location area model.In these two incentive mechanisms,each task has a sensing area.In the incentive mechanism for location coordinate model,the user’s position is a fixed specific coordinate point.If the sensing area of the task can cover the user’s coordinate position,the user can participate in the task;while in the incentive mechanism for location area model,the user has a piece of its own active area,and the user can perform tasks at any position in the overlapped area between the task sensing area and the user’s active area.Both mechanisms include two steps: winner selection and payment determination.In the winner selection stage,the winner is determined by calculating the minimum average marginal cost,and in the payment determination stage,the reward of the winner is determined by calculating the key payment.Through rigorous theoretical analysis and simulation experiments,it is proved that the incentive mechanisms proposed in this article achieve computational efficiency,individual rationality,truthfulness and guaranteed approximation.(2)Considering that the accuracy of the location data submitted by the user has an important impact on the data quality perceived by the mobile crowdsensing.The data sensed in incorrect locations is often not accurate,which reduces the service quality of mobile crowdsensing.In order to improve the robustness of the mobile crowdsensing system,on the basis of the abovementioned incentive mechanism,this paper designs a coordinate outlier detection algorithm and an area outlier detection algorithm respectively,and compares the bidding location and the historical location submitted by the user.When the deviation between the bidding position and the historical position exceeds the threshold,the user is determined to be an abnormal user and removed from the set of bidding users to improve the robustness of the system.The extensive experiments have shown that coordinate outlier detection algorithms and area outlier detection algorithms can improve the robustness of the mobile crowdsensing systems efficiently.