Quality-Aware Incentive Mechanism for Mobile Crowdsourcing

Recent years have witnessed the emergence of mobile crowdsourcing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large-scale sensing tasks. In this chapter, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from the existing work, we design two quality-aware incentive mechanisms, and we incorporate a crucial metric, called users’ quality of information (QoI), in the first quality-aware incentive mechanism and consider the preservation of users’ bid privacy in the second quality-aware incentive mechanism for MCS system. Due to various factors (e.g., sensor quality, noise, etc.), the quality of the sensory data contributed by individual users varies significantly. Obtaining high-quality data with little expense is always the goal of a quality-aware incentive mechanism for MCS system. Besides, the data from users usually contains the private information that should not be disclosed. A quality-aware incentive mechanism should consider the preservation of users’ bid privacy. Technically, we design the first quality-aware incentive mechanism based on reverse combinatorial auctions. We investigate both the single-minded and multi-minded combinatorial auction models and design two computationally efficient mechanisms that the one for single-minded models can approximately maximize social welfare and the one for multi-minded models can achieve close-to-optimal social welfare. We design the second quality-aware incentive mechanism based on the single-minded reverse combinatorial auction that preserves the privacy of each workers bid against the other honest-but-curious users. Specifically, we design a private, individual rational, and efficient mechanism that approximately minimizes the platforms’ total payment and satisfies the desirable economic properties of approximate truthfulness and individual rationality.