Sensors on mobile phones can enable attractive sensing applications in different domains such as environmental monitoring, social networking, healthcare, etc. However, fundamental energy-efficient resource management problems have not been well studied for mobile phone sensing. In addition, how to provide incentives to attract user participation has not been well addressed. The objective of this project is to develop a unified and green platform for mobile phone sensing, optimize its performance by designing energy-efficient algorithms for sensing task management, and develop game-theoretic incentive mechanisms to attract user participation. The proposed research is organized into four cohesive research thrusts:
- Design and implement a unified software architecture to enable support for various sensing applications.
- Develop both platform-centric and user-centric incentive mechanisms to attract user participation.
- Develop energy-efficient algorithms to manage sensing tasks.
- Test the developed platform and algorithms via simulation and real experiments.
This research will result in a unified and green mobile phone sensing system. Fundamental resource management problems will be solved by theoretically-sound and practical algorithms. The project will also result in novel incentive models and mechanisms for mobile phone sensing. In addition, the proposed platform can create a completely new type of online marketplace. The proposed energy-efficient algorithms can benefit both mobile users and the environment. The project is also expected to advance public understanding of mobile phone sensing via publications, seminars and workshops.
This research is funded by NSF under grants #1218203 (Syracuse University) and #1217611 (Arizona State University).
Key Personnel:
Syracuse University
- Jian Tang (PI)
- Xiang Sheng (Graduate Assistant)
- Jing Wang (Graduate Assistant)
- Teng Li (Graduate Assistant)
- Zhenhua (Jimmy) Chen (Graduate Assistant)
- Jielong Xu (Graduate Assistant)
Arizona State University
- Guoliang Xue (PI)
- Dejun Yang (Graduate Assistant)
- Lingjun Li (Graduate Assistant)
- Xiang Zhang (Graduate Assistant)
- Ruozhou Yu (Graduate Assistant)