Project Proposed: This project, developing a Hybrid Wireless Network (HWN) to be built on top of an existing Heterogeneous Sensor Network (HSN) testbed, supports research in broadband wireless networking and communication enabling high-quality and high-accuracy performance evaluations of protocols and schemes designed for HWNs. Research projects include - Efficient Quality of Service (QoS) provisioning and - Orthogonal Frequency Division Multiple Access (OFDMA) resource allocation in HWNs. These projects are expected to satisfy individual user's QoS requirements while optimizing the performance (e.g., throughput) of the overall system. Although QoS support in traditional wireless networks and OFDMA resource allocation in cellular networks have been well studied, in HWNs QoS routing and OFDMA resource allocation are highly correlated and the problem lends itself to further exploration. The HWN services faculty and students in Computer Science and Electrical Engineering and 65 staff researchers at the Center for Nanoscale Science and Engineering at the institution and nearby universities. The work aims at integrating various aspects of educational and training environment with innovative research into a cohesive package for education in wireless networking and communication.
Broader Impacts: Supported education and training activities include - Recruiting students of underrepresented groups to participate in the research, as well as mentoring - Developing a new course in Wireless Networking and Communications - Integrating research and education Native American, female, low income, and first generation students will be specifically targeted in this EPSCoR state where 31% of the undergraduate populations falls below the threshold for low-income US citizens.
Project Outcomes Intellectual Merit The major goal of this project is to develop a Hybrid Wireless Network (HWN) infrastructure. The HWN infrastructure consists of WiMAX base station, a number of mobile stations (smart phones, tablets, and laptops), one computing server, one storage server, and some related devices. The HWN infrastructure has been used to supported research and education activities in wireless networks. During the award period, we have finished the following research activities: 1)We developed efficient bandwidth allocation schemes for HWNs 2) Wireless mesh network (WMN) is a rapid deployed, self organized and multi-hop hybrid wireless networks. We designed an efficient key management scheme for WMN. 3) We designed an efficient and secure re-keying scheme for IEEE 802.16e WiMAX networks. 4) We studied efficient communications in Mobile Hybrid Wireless Networks (MHWNs). 5) We studied channel switching control policy for wireless mesh networks. 6) We investigated interference mitigation techniques for macro/femto heterogeneous wireless networks. 7) We studied the coexistence and spectrum sharing of heterogeneous wireless networks. 8) We investigated fair spectrum sharing in cognitive wireless networks. 9) We studied distributed precoder design for inter-cell interference suppressing in multi-cell MU-MIMO wireless systems. Broader Impacts During the award period, we have published 7 journal papers and 15 conference papers. We disseminated our research results through these peer-reviewed publications. The research in this project is concerned about innovative and efficient network architecture, energy efficient networking protocols for hybrid wireless networks. Our findings and techniques have impacts to many disciplines (other than CS/EE) where hybrid wireless networks have applications, such as homeland security, health care, and environment. Some of the above research results have been presented at one graduate course - CIS 8537 Network and Information Security, and two undergraduate course - CIS 3319 Wireless Networks and Security & CIS 4319 Computer Networks and Communications at Temple University. During the award period, two female Ph.D. students have been supported by the grant. In addition, a few female undergraduate students and one black undergraduate student have been supported by the REU supplements of this grant. The students on this project are trained on a variety of cross-cutting disciplines. These include network modeling and optimization, probabilistic analysis, software development, and experimentation on real systems. This multi-disciplinary training helps them be better prepared for both industry and academia.
