Qun Li, an assistant professor in the department, recently received an NSF CAREER grant titled Advanced Data Management for Sensor Networks.
From the NSF description:
The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation's most prestigious awards in support of the early career-development activities of those teacher-scholars who most effectively integrate research and education within the context of the mission of their organization. Such activities should build a firm foundation for a lifetime of integrated contributions to research and education.
As an extension to the cyber-space, sensor networks (or more general networks of small devices) serve as a layer through which people can interact with the physical world. This is especially true for pervasive computing environment where tiny, inexpensive chips are embedded in devices around us. Those devices will store, collect, and process a large amount data in various formats. It is a challenge, however, to extract information from this widely distributed, massively accumulated, mixed and somehow unstructured data in an efficient and secure fashion. This project aims to address this challenge by building an advanced sensor network data management system.
The major effort of this project includes the following three components. First, we are building a search engine for the physical world. Rather than search the documents distributed on the Internet or stored in a computer, this search engine will search the documents distributed in small devices attached to the physical objects. Second, we are designing efficient data mining algorithms for sensor networks to provide abstracted, useful and meaningful information to the end user. Third, we are building a security infrastructure (including WM-ECC, an efficient ECC security suite implemented at W&M) for small devices and working on various issues in security and privacy.