Make a Difference: Diversity-Driven Social Mobile Crowdsensing

In a mobile crowdsensing (MCS) application, user diversity and social effect are two important phenomena that determine its profitability, where the former improves the sensing quality, while the latter incentives the users' participation. In this paper, we consider a reward mechanism design for the service provider to achieve diversity in the collected data by exploiting the users' social relationship. Specifically, we formulate a two-stage decision problem, where the service provider first optimizes its rewards for profit maximization. The users then decide their effort levels through social network interactions as a participation game. The analysis is particularly challenging due to the users' interplay in both the diversity and social graphs, which leads to a non-convex bilevel optimization problem. Surprisingly, we find that the service provider can focus on one superimposed graph that incorporates the diversity and social relationship and compute the optimal reward as the Katz centrality in closed-form. Simulation results, based on the random graph and a real Facebook trace, show that the availability of network information improves both the service provider's profit and the users' social surplus over the incomplete information cases.