Truthful Incentive Mechanism for Budget-Constrained Online User Selection in Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) has attained much attention for gathering distributed mobile users to complete large-scale sensing tasks. To ensure the task completion, enough users should be motivated to participate in sensing tasks. Thus, much research in MCS focuses on proposing incentive mechanism, and these works usually focus on the offline scenario, where the information of all users is available to the platform. However, we argue that the actual MCS is usually an online scenario, where the platform does not know the user's information until they establish connections with the platform. Meanwhile, mobile users connect to the platform randomly and will cut off the connection at any time. Hence, when accessed by a user, the platform needs to make an irrevocable decision instantly about whether to select the user or not, and decides a remuneration for the user without knowing future information. In this paper, we first propose a reverse-auction framework to model the interaction between the platform and mobile users. Then, we present an online truthful incentive mechanism (OTIM) to motivate users, including online winner selection and remuneration determination strategies. Finally, massive simulations are conducted based on three real traces, and the simulation results illustrate that OTIM achieves truthfulness, individual rationality, computational efficiency and an approximately full budget utilization.