基于“众包”思想的群智感知系统以更低的成本完成传统传感器网络难以应对的大规模、复杂的感知任务。然而，感知数据质量难以保证将制约群智感知系统的广泛应用。影响数据质量的因素贯穿整个任务过程：执行前，需招募并评估参与者，以选择优质用户；执行中，需检测设备感知上下文信息是否满足任务约束，以进行数据筛选；执行后，需通过高效的传输方式进行数据收集，以改善现有数据收集方式存在的流量成本高、接入机会不足的问题。本项目考虑用户自私性，设计在线激励机制，以吸引足够的优质用户参与；评估用户质量，设计用户选择模式以保证感知任务能够快速响应；基于音频感知，提取用户间的相对几何约束，以提高用户定位精度；提取多径效应特征，建立设备工作环境识别模型；挖掘用户社会属性及时空特性，设计满足低成本、低功耗、低时延目标的数据收集机制。研究成果能够为群智感知数据质量保证技术的实践应用奠定坚实的理论和技术基础。

Crowd sensing is a technique that crowdsources sensor data from a large group of individuals equipped with mobile devices which are capable of sensing and computing. Mobile crowd sensing system has enabled advance and larger-scale sensing with richer functionality, which is not adaptable in traditional wireless sensor network. Many works have been done by researchers in this field, however there still exist some unsolved research issues of quality control. The research goal of this project is to improve the quality of sensed data during the whole sensing process. Specifically, this project includes three stages. In the pre-sensing stage, we design task allocation and incentive mechanisms to select appropriate users with high quality of services. In the sensing stage, we implement context-aware sensing systems to achieve high quality data. In the post-sensing stage, we propose data delivery methods to efficiently collect high quality data. To address the challenging issues, this project considers social behaviors of users and design online incentive mechanisms to attract sufficient high quality users; evaluates quality of services of users to design user selection systems with quick response; leverages frequency responses of recorded vibration sound changes to improve localization accuracy; extracts multipath features from acoustic signal to identify contexts; utilizes time-evolving and social characters to propose low cost, energy efficient, short delay, and robust data collection methods. This project also provides solid theoretical analysis and plentiful experiments to evaluate the performance. The achievements of this project will provide profound fundamental theory and technical support for the development of mobile crowd sensing system.