BundleSense: A Task-Bundling-Based Incentive Mechanism for Mobile Crowd Sensings

Abstract

Mobile crowd sensing (MCS) has become a powerful sensing paradigm that allows requesters to outsource location-based sensing tasks to a crowd of participating users carrying smart mobile devices. Aware of the paramount importance of incentivizing participation for MCS systems, researchers have proposed a wide variety of incentive mechanisms. Most of these mechanisms assume that the MCS platform can collect sufficient budget to recruit users, and hence only focus on incentivizing users efficiently. In this work, we consider MCS systems where the budget of a single task is insufficient for recruiting a user. Commonly, a task requester with a simple task (e.g., inquiring a photo of a restaurant) is willing to provide a low budget, while a user would like to earn a higher reward for his effort in completing a task (e.g., traveling a long distance to take a photo). To address this disparity issue between requesters and users, we propose a novel task-bundling-based two-stage incentive mechanism to incentivize both requesters and users. Through rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanism satisfies the properties of computational efficiency, individual rationality, budget balance, truthfulness, and constant competitiveness.