| In the big data Era,mobile crowdsensing has become an efficient paradigm for performing largescale sensing tasks.It has been a research hotspot in the fields of wireless communication and sensor technology.Mobile crowdsensing requires a large number of users to participate,and then uses the wisdom of the groups to complete the tasks,which are usually difficult to complete for individual.In the mobile crowdsensing system,the incentive mechanism plays an important role in attracting participants and improving the quality of perceived data.Therefore,it is necessary to design different incentive mechanisms for different application scenarios and different targets.In this thesis,we explore truthful incentive mechanisms focusing on minimizing the total payment for a novel scenario,where the platform needs the integrated sensing data in a Requested Time Window(RTW).We model this scenario as a reverse auction and design a constant Frugal Incentive Mechanism for tIme window coverage(FIMI).FIMI consists of two phases,the candidate selection phase and the winner selection phase.In the candidate selection phase,it selects two most competitive disjoint feasible user sets.Afterwards,in the winner selection phase,it finds all the interchangeable user sets through the graph-theoretic approach.For every pair of such user sets,FIMI chooses one of them according to the weighted cost.Further,we extend FIMI to the scenario,where the RTW needs to be covered more than once.Through both rigorous theoretical analysis and extensive simulations,we demonstrate that the proposed mechanisms achieve the properties of RTW feasibility(or RTW multi-coverage),computation efficiency,individual rationality,truthfulness,and constant frugality. |
