Incentive Mechanism with Task Bundling for Mobile Crowd Sensing

Abstract

Mobile crowd sensing (MCS) has become a powerful sensing paradigm that allows requesters to outsource sensing tasks to a crowd of mobile users. Aware of the paramount importance of incentivizing participation for MCS, researchers have proposed various incentive mechanisms. Most mechanisms assume that the MCS platform can collect sufficient budget to recruit users, and hence only focus on incentivizing users. In this work, we consider MCS systems where the budget of a single task is insufficient for user recruitment. Commonly, a task requester with a simple task (e.g., inquiring a photo of a restaurant) only provides a small budget, while a user wants to earn a larger reward for his effort (e.g., traveling a long distance to take a photo). To address this disparity issue, we propose novel task-bundling-based two-stage incentive mechanisms to incentivize both requesters and users. Specifically, tasks are first clustered as bundles, where the budgets in one bundle are collected through a random partition method. Then, a double auction is conducted, which sorts budgets and bids to maximize matching. Through theoretical analysis and extensive evaluations on synthetic and real-world datasets, we demonstrate that the proposed mechanisms satisfy computational efficiency, individual rationality, budget balance, truthfulness, and constant competitiveness.