Management of Mobile Phone Sensing via Sparse Learning

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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.