Management of Mobile Phone Sensing via Sparse Learning

今天的智能手机带有各种内置传感器，如GPS、加速计和摄像头来获取信息。这些传感器还使智能手机能够按需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，这种类型的手机感知可以实现无处不在的覆盖。它还具有成本效益，因为它不需要专用的传感基础设施，并且可以更灵活地适应不同的传感需求。移动电话感知系统的主要设计问题是最小化电池使用/能源消耗，并减少对智能手机主要语音和数据服务的干扰，同时保持所需的感知服务质量。这只需要激活最适合传感任务的一部分智能手机。这样的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，通常是难以解决的。本项目的目标是设计一种全面的基于稀疏学习的方法，将具有挑战性的组合传感器选择问题转化为稀疏性鼓励的可有效求解的凸优化问题。研究议程将包括针对传感器协作结构开发稀疏学习方法，设计基于激励的机制来满足传感器需求和单个传感器的利润，以及设计适应时变环境和传感器激励的动态管理策略。该项目的成功将极大地推进传感器管理设计，并潜在地显着提高实用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.