Management of Mobile Phone Sensing via Sparse Learning

如今的智能手机内置各种传感器，如GPS、加速度计和摄像头来获取信息。这些传感器还使智能手机能够根据需要共同完成各种传感服务，例如环境监测和某些地理区域的交通测量和控制。由于智能手机的移动性，这种类型的移动电话传感可以实现无处不在的覆盖。它也具有成本效益，因为它不需要专用的传感基础设施，可以更灵活地适应不同的传感需求。移动电话传感系统的主要设计问题是尽量减少电池使用/能源消耗，减少对智能手机主要语音和数据服务的干扰，同时保持所需的传感服务质量。这只需要激活最适合传感任务的智能手机子集。这种传感器选择问题对于大规模网络来说是非常具有挑战性的，因为它们本质上是组合的，通常是难以处理的。本项目的目标是设计一种基于稀疏学习的综合方法，将具有挑战性的组合传感器选择问题转化为可有效解决的稀疏性鼓励凸优化问题。研究议程将包括基于传感器协作结构的稀疏学习方法的发展，基于激励机制的设计，以满足传感需求以及单个传感器的利润，以及适应时变环境和传感器激励的动态管理政策的设计。该项目的成功将大大推进传感器管理设计，并有可能显著提高实际移动电话传感系统的效率。该项目将为来自不同背景的学生提供实践培训机会。

Today's smartphones carry various built-in sensors such as GPS, accelerometers, and cameras to acquire information. These sensors also enable smartphones to collectively accomplish various sensing services on demand, for example environmental monitoring and traffic measurement and control over certain geographical areas. This type of mobile phone sensing allows ubiquitous coverage due to mobility of smartphones. It is also cost effective, as it does not need a dedicated sensing infrastructure and can adapt more flexibly to different sensing demands. The major design issue for mobile phone sensing systems is to minimize battery usage/energy consumption and reduce interference with primary voice and data services of smartphones while maintaining the desired quality of sensing services. This requires activating only a subset of smartphones that best serve the sensing task. Such sensor selection problems are very challenging for large-scale networks, as they are combinatorial in nature and intractable in general. The goal of this project is to design a comprehensive sparse learning based methodology, which converts challenging combinatorial sensor selection problems to sparsity-encouraged convex optimization problems that can be solved efficiently. The research agenda will include development of sparse learning approaches in view of sensor collaboration structures, design of incentive-based mechanisms to fulfill sensing demands as well as individual sensors profits, and design of dynamic management policies that adapt to time-varying environments and incentives of sensors. Success of this project will greatly advance sensor management designs and potentially significantly improve efficiency of practical mobile phone sensing systems. This project will offer hands-on training opportunities to students from diverse backgrounds.