On Designing Quality-Aware Steering Algorithms for Large-Scale Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS), as a novel and promising sensing paradigm, can utilize people's mobile devices to gather large amounts of data, such as environment information, traffic conditions, and human movements. The users of mobile crowdsensing are usually more capable than traditional sensors, and can reach locations that cannot be easily covered by static sensors, achieving more comprehensive coverage than traditional sensor networks. However, the uncertainty of the users' behaviors, as well as their uneven levels of qualities of contributed data, may also bring challenges to the coordination and supervision of mobile crowdsensing, causing the effectiveness of crowdsensing platform to significantly deviate from the theoretical optimum. In this paper, we address the users' uncertain behaviors by considering a quality- aware user steering problem, and propose to design user coordination algorithms so as to improve the mobile crowdsensing system's overall effectiveness. We jointly take two issues into account, i.e., data quality and coverage of sensing area, and propose a characterization of the system's effectiveness based on the two factors. Next, we consider optimizing the system's effectiveness in three different practical crowdsensing scenarios, and prove the NP-hardness of each of them. Given the infeasibility of calculating the global optimum in polynomial time, we propose three efficient algorithms to achieve suboptimal solutions to the three problems respectively. We extensively evaluate our proposed algorithms based on both real and synthetic datasets. The evaluation results show that our proposed algorithms can dramatically improve the crowdsensing system's effectiveness.