User Recruitment for Enhancing Data Inference Accuracy in Sparse Mobile Crowdsensing

Abstract

Sparse mobile crowdsensing is a practical paradigm for large sensing systems, which recruits a small number of users to sense data from only a few subareas and, then, infers the data of unsensed subareas. In order to provide high-quality sensing services under a budget constraint, we would like to select the most effective users to collect useful sensing data to achieve the highest inference accuracy. However, due to the variable user mobility and complicated data inference, it is really challenging to directly select the best user set which helps the most with data inference. From the user's side, we can obtain the probabilistic coverage according to the users' mobilities, while the probabilistic coverage cannot indicate the data inference accuracy directly. From the subarea's side, we may identify some more useful subareas under the current states (e.g., the previous sensed subareas and the current expected coverage), while these useful subareas may not be covered by the users. Moreover, both the user mobility and data inference introduce a lot of uncertainty, which yields nonmonotonicity and thus nonsubmodularity in the user recruitment problem. Therefore, in this article, we study the user recruitment problem on both the user's and subarea's sides and propose a three-step strategy, including user selection, subarea selection, and user-subarea-cross (US-cross) selection. We first select some candidate user sets, which may cover the most subareas under the budget constraint (user selection), then estimate which subareas are more useful on data inference according to the selected candidates (subarea selection), which finally guides us to recruit the best user set (US-cross selection). Extensive experiments on two real-world data sets with four types of sensing tasks verify the effectiveness of our proposed user recruitment algorithms, which can effectively enhance the data inference accuracy under a budget constraint.