EGAIM: Enhanced Genetic Algorithm based Incentive Mechanism for Mobile Crowdsensing

Mobile Crowdsensing (MCS) systems take advantage of the ubiquity and sensing power of smartphones in data gathering. Designing an incentive mechanism for motivating the individuals to participate in such systems is vital. Reverse auction is a popular incentive framework in which the users bid their expected returns for their contributions, and the mechanism then selects a number of them as the participants based on their value for the system. In this paper, we consider the goal of participant selection as maximising the total contribution within a budget constraint where the user contributions may be disparate and coverage overlap is possible. We propose a genetic algorithm approximation solution for this optimisation problem. We call the mechanism as Genetic Algorithm based Incentive Mechanism (GAIM). We also propose an enhanced version of this approach (EGAIM) in which an improved parent selection strategy is utilised to overcome two limitations of GAIM which arise in situations where the budget is limited. We compare EGAIM with GAIM and a greedy algorithm under two real-world scenarios, and show that using EGAIM can save up to 55% of budget for achieving at the same level of contribution.