1 Form And Nature Of Students Involvement In Research As the largest university in the USA, Arizona State University (ASU) is home to many talented domestic undergraduate students. However, retaining them in careers in science and engineering is a challenge. The PI taught CSE310Data Structures and Algorithms (a junior level course with around 150 students) last semester. Outside of the classroom, the PI often describes his research projects to his undergraduate students and invites them to visit his research lab, with the hope that most of the talented undergraduate students will go on to graduate schools and eventually become researchers in science and engineering. The PI has identified some talented students (US citizens) as potential candidates to participate in his research project (NSF 1217611: A Green and Incentive Platform For Mobile Phone Sensing). These potential candidates have already learned many advanced data structures and algorithm design techniques such as Divide and Conquer, Dynamic Programming, and Greedy Algorithms. Letting them having first-hand experience in leading research projects will have a huge impact in their careers. If the Supplement is funded, the selected students will work in the PIs research lab starting in the summer of 2013. Some of them may continue to work in fall of 2013 and spring of 2014, depending on availability of funds. The student will have access to the computing facilities in the PIs lab and those of the School of Computing, Informatics, and Decision Systems Engineering at ASU. The REU students will participate in the weekly research seminars with the PI and his graduate students over the summer, and in the following academic year. The proposed research of the on-going NSF project is on mobile phone sensing. Within the first eight months of the project, the PIs group has already produced three high quality papers [1, 2, 3] in major conferences. In , we have designed novel incentive mechanisms for mobile phone sensing. In , we have outlined the architecture and research challenges for mobile phone sensing. In , we have designed novel biometric based active authentication schemes for smartphones. It is expected that the REU students will be heavily involved in the implementation of new algorithms and protocols in this area. The REU students will benefit greatly from the participation of these interesting projects. The REU students will also participate in the weekly group meetings of the PI, and interact with the graduate students of the PI. Hence they will learn the state of the arts in this area. 2 Experience Of The PI As a Computer Science faculty member, the PI has always been interested in involving undergraduate students in research. The PI has had several REU Supplements on previous NSF grants. Two years ago, the PI recruited and supervised a Hispanic REU student Gabriel Silva. Gabriel participated in most of the PIs group research meetings, and published as a co-author in two top-tier conference papers [4, 5]. Gabriel Silva joined Microsoft as a software engineer upon graduation. Another former REU student (Chris Chandler) was also involved in high quality publications [6, 7]. In addition to dedication to high quality research, the PI is also a dedicated mentor who cares about the future of his students. In 2004, the PI was honored by ASU Student Affairs as a leader, a mentor, and a person who has contributed in a significant way to the success of ASU students (http://optimization.asu.edu/xue/ASU-VPSA-Honor.jpg). A former PhD student (Jian Tang) of the PI is now on the faculty of Syracuse University and is a recipient of the NSF CAREER (NSF-1217611 REU Justification)1 Award. Another former PhD student (Satyajayant Misra) is on the faculty of New Mexico State University and is a recipient of the NSF Creativ Award. In addition, the PI has recently recruited Margaret Todd (a black female student) as an MS student. 3 Criteria For Student Selection The PI is requesting for support of two REU students. While teaching CSE310Data Structures and Algorithms in Fall of 2012, the PI is supervising several Honors Projects. These include the following students. Aaron Baker Terrance Williams Matthew Yanez These students will be the natural candidates for the REU positions. The PI will also post an announcement of the REU opportunity at the class website (which students are supposed to check each day) and invited interested students who have top 10% standing in the class to submit a brief resume and a transcript. Depending on the funding, the PI will further narrow down to two of them, or to find funding from other sources to get as many of them involved in his research as possible.
Most of current smart phone models are equipped with a rich set of embedded sensors. Moreover, External sensors can also be connected to a mobile phone via its Buletooth interface. These sensors can enable sensing applications in different domains such as environmental monitoring, social network, healthcare. This project will be focused on a sensor network composed of a large number of mobile phones. Systems, protocols and algorithms proposed for mobile sensor networks cannot be applied in such a network since they usually assume that mobility of sensors can be controlled to provide desired sensing coverage. However, in mobile phone sensing scenarios, sensors mobility is totally uncontrollable. In addition, performing sensing tasks using a mobile phone may consume significant amount of energy. Therefore, without carefully managing very limited energy resources on mobile phones, mobile users may end up with an awkward situation after performing a few sensing tasks, in which phones are out of battery when they are needed to make phone calls. The objective of this project is to develop a unified, green and and incentive platform for mobile phone sensing, optimize its performance by designing energy-efficient algorithms for sensing task management, and developing game-theoretical incentive mechanisms for attracting mobile phone users. Specifically, a unified platform will be developed to support various opportunistic and participatory sensing applications. Moreover, the platform will be reconfigurable such that any sensing task management policy or algorithm can be be easily deployed on the platform. In addition, sensing task management algorithms will be developed to optimize its performance in terms of energy consumption and game-theoretical incentive mechanisms to attract mobile phone users to participate in sensing tasks.
|Effective start/end date||8/1/12 → 7/31/15|
- National Science Foundation (NSF): $226,000.00