Data-Centric Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel and appealing sensing paradigm that leverages the diverse embedded sensors of massive mobile devices to collect different kinds of data. One of the key challenges in MCS is to efficiently schedule mobile device users to perform different sensing tasks. Prior effort to this problem mainly focused on the interaction between the task-layer and the user-layer, without considering the similar data requirements of tasks and the heterogeneous sensing capabilities of users. In this work, we introduce a new data-layer between tasks and users, and propose a three-layer data-centric MCS framework, which enables different tasks to reveal their common data requirements and hence reuse the common data items. We focus on studying the joint task selection and user scheduling problem under this new framework, aiming at maximizing the social welfare. Specifically, we first analyze theoretical performance gain due to data reuse in the ideal scenario with complete information. We then consider the practical scenario with private information of both tasks and users, and propose a two-sided randomized auction mechanism, which is computationally efficient, individually rational, incentive compatible (truthful) in expectation, and close-to-optimal. We further show that the proposed randomized auction may not be budget balanced, and hence introduce a reserve price into the auction to achieve the desired budget balance at the cost of certain welfare loss. Simulation results show that with data reuse, the social welfare achieved in the proposed randomized auction can be increased from 270 up to 4,500 percent, comparing with those without data reuse.

移动众包感知（MCS）是一种新颖且有吸引力的感知范式，它利用大量移动设备中多样的嵌入式传感器来收集不同类型的数据。MCS中的关键挑战之一是有效地安排移动设备用户执行不同的感知任务。先前针对该问题的努力主要集中在任务层和用户层之间的交互上，没有考虑任务相似的数据需求以及用户异构的感知能力。在这项工作中，我们在任务和用户之间引入一个新的数据层，并提出一个以数据为中心的三层MCS框架，该框架使不同的任务能够揭示其共同的数据需求，从而复用公共数据项。我们重点研究在这个新框架下的联合任务选择和用户调度问题，旨在最大化社会福利。具体而言，我们首先分析在具有完全信息的理想情况下由于数据复用所带来的理论性能增益。然后我们考虑任务和用户都具有私有信息的实际情况，并提出一种双边随机拍卖机制，该机制在计算上是高效的，个体理性的，期望上是激励相容（诚实）的，并且接近最优。我们进一步表明，所提出的随机拍卖可能不是预算平衡的，因此在拍卖中引入保留价格以实现期望的预算平衡，但会以一定的福利损失为代价。仿真结果表明，通过数据复用，与没有数据复用的情况相比，所提出的随机拍卖中实现的社会福利可以从270%提高到4500%。