Mobile Crowdsourcing (MCS) is a novel distributed computing paradigm that recruits skilled workers to perform location-dependent tasks. A number of mature incentive mechanisms have been proposed to address the worker recruitment problem in MCS systems. However, they all assume that there is a large enough worker pool and a sufficient number of users can be selected. This may be impossible in large-scale crowdsourcing environments. To address this challenge, we consider the MCS system defined on a location-aware social network provided by a social platform. In this system, we can recruit a small number of seed workers from the existing worker pool to spread the information of multiple tasks in the social network, thus attracting more users to perform tasks. In this paper, we propose a Multi-Task Diffusion Maximization (MT-DM) problem that aims to maximize the total utility of performing multiple crowdsourcing tasks under the budget. To accommodate multiple tasks diffusion over a social network, we create a multi-task diffusion model, and based on this model, we design an auction-based incentive mechanism, MT-DM-L. To deal with the high complexity of computing the multi-task diffusion, we adopt Multi-Task Reverse Reachable (MT-RR) sets to approximate the utility of information diffusion efficiently. Through both complete theoretical analysis and extensive simulations by using real-world datasets, we validate that our estimation for the spread of multi-task diffusion is accurate and the proposed mechanism achieves individual rationality, truthfulness, computational efficiency, and $(1-1/\sqrt{e}-\varepsilon)$ approximation with at least $1-\delta$ probability.

翻译：移动众包（MCS）是一种新型分布式计算范式，通过招募技能工人执行位置相关任务。现有众多成熟的激励机制已解决MCS系统中的工人招募问题，但这些机制均假设存在足够大的工人池并可筛选出足够数量的用户。在大型众包环境中，这一假设可能无法成立。为应对这一挑战，我们考虑在社交平台提供的基于位置感知的社交网络上定义MCS系统。在该系统中，我们从现有工人池中招募少量种子工人，通过社交网络传播多个任务的信息，从而吸引更多用户执行任务。本文提出多任务扩散最大化（MT-DM）问题，旨在预算约束下最大化执行多个众包任务的总效用。为适应社交网络中的多任务扩散，我们构建了多任务扩散模型，并基于该模型设计了基于拍卖的激励机制MT-DM-L。针对多任务扩散计算的高复杂度问题，我们采用多任务反向可达集（MT-RR）高效近似信息扩散效用。通过完备的理论分析与基于真实数据集的广泛仿真，我们验证了多任务扩散传播估计的准确性，并证明所提机制满足个体理性、真实性、计算高效性，且能以至少1-δ的概率实现(1-1/√e-ε)的近似比。