Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Different from existing work, we propose an incentive framework for MCS systems, named Thanos, that incorporates a crucial metric, called workers’ quality of information (QoI). Due to various factors (e.g., sensor quality and environment noise), the quality of the sensory data contributed by individual workers varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational, and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio. Through extensive simulations, we validate our theoretical analysis on the various desirable properties guaranteed by Thanos.