Truthful User Recruitment for Cooperative Crowdsensing Task: A Combinatorial Multi-Armed Bandit Approach

Abstract

Mobile Crowdsensing (MCS) is a promising paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on users objective abilities, e.g., the probability or frequency of covering the task locations. However, we argue that the task completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. Even though we can find a well-behaved group of users in the single-round scenario, while in the multi-round scenario without enough prior knowledge, we still face the problem of exploiting users or exploring users. Additionally, we consider that users have different costs and may report false costs to gain more benefits. To address these problems, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the approximate solution. Then, in the multi-round scenario, we propose a multi-round User Recruitment strategy under the budget constraint based on the combinatorial Multi-armed Bandit model (URMB), which achieves a tight regret bound. Next, we propose a graph-based payment strategy to ensure the truthfulness and individual rationality. Finally, experiments based on real-world datasets show that URMB always outperforms the state-of-the-art strategies.