Incentive Mechanism Design For Mobile Crowdsensing Based On Social Network And Spatial-temporal Tasks

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks and a new application mode and development trend of Internet.Mobile crowdsensing depends on the participation of mobile users.Mobile users collect and submit data to platform with a variety of sensors in mobile phone.When the mobile users collect the data,their resources such as time,vigor,and energy of their smartphones will be consumed.Therefore,it is vital for mobile crowdsensing to design the effective incentive mechanism.In this thesis,we major focus on incentive mechanism design for mobile crowdsensing based on social network and spatial-temporal tasks.First,we focus on solving the insufficient participation problem in the budget constraint online crowdsourcing system.We present a two-tiered social crowdsourcing architecture,which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles.In the two-tiered social crowdsourcing system,the tasks are associated with different end times,and both the registered users and their social neighbors have different arrival/departure times.An online incentive mechanism,MTSC,which consists of two steps: Agent Selection and Online Reverse Auction,is proposed for this novel mobile crowdsourcing system.Moreover,different from existing work of mechanism design,we consider the special case of spatio-temporal tasks in mobile crowdsensing systems,where the sensing areas of tasks can have overlaps,and the collective sensing time for each task needs to meet the specified time duration..In this thesis,we introduce a reverse auction framework to model the interactions between the platform and the smartphones.We design two mechanisms called MLS and MLI.In MLS,the user is location sensitive.The user just can participate in the tasks which his location is in the AoIs(Area of Interests)of tasks.In MLI,the user is location is insensitive.The user has its active area,and can go any position in the active area to participate in the tasks.Through both rigid theoretical analysis and extensive simulations,we demonstrate that the proposed mechanism achieves truthfulness,individual rationality and high computation efficiency.