Cognitive Wireless Systems and Networks
Cognitive wireless systems describe various networks that may differ in scales and service objectives, but share the common attribute that nodes in the wireless network as well as their operations are autonomous or semi-autonomous. Each node is cognizant both of its surrounding environment (in both physical and cyber sense) as well as of the service objective such that the operation and efficiency of the network do not rely on some centralized and intelligent controller.
The broadly defined topic of cognitive wireless systems is a research area that has a sustainable future; `smart’ devices and networks are increasingly becoming an integrated part of our daily lives. Equally important, cognitive networks and cognitive systems are becoming indispensable components of various tactical networks in which US federal government agencies have vested interest. Such networks go beyond the traditional cognitive radio networks and also encompass networks that include myriads of nodes of different modality and with diverse applications. Examples include cognitive sensing networks with smart sensing nodes with applications to DoD domain problems as well as environmental infrastructure and human health monitoring; mobile phone networks where autonomous and nomadic devices form impromptu networks for information gathering or other purposes; smart camera networks; mobile cloud computing, and smart utility networks.
This research area brings together the College’s current activities in wireless communications theory (signal processing, communications, information theory and networking) with both commercial and DoD applications. It builds on the group’s strong research relationships with the Air Force Research Laboratory and researchers at Princeton, UIUC, MIT, and other academic institutions. Through this Strategic Plan, the group will expand its strengths to include expertise in system integration, optimization, and cognitive science, and will provide greater leadership in spearheading multi-disciplinary research projects where cognitive wireless networks play a central role and with diverse applications ranging from defense and security to health, energy and environmental systems.
Biao Chen’s research focuses on spectrum efficient communications for wireless networks, sensor networks with mobile and cognitive nodes and information fusion and decentralized inference.
M. Cenk Gursoy’s research interests are in the general areas of wireless communications, information theory, communication networks, and signal processing. He has been working on the fundamental performance limits of wireless communications, energy-efficient transmission schemes, optimal resource allocation strategies, quality of service provisioning in wireless networks, cognitive radio systems, physical layer wireless security, and cooperative communications.
Jian Tang’s research interests lie in the areas of Wireless Networking and Cloud Computing. He developed efficient resource allocation algorithms and protocols for emerging wireless networks such as cognitive radio networks and 4G wireless communication systems. In addition, he is currently building a unified and green cloud computing platform for mobile phone sensing.
Senem Velipasalar’s primary area of research is wireless embedded smart cameras. She has been working on designing resource-efficient algorithms that are suitable for embedded platforms, target detection and tracking, resource allocation strategies and detection of events of interest on wireless embedded smart cameras. Potential uses include military surveillance, public transportation, health care and elder care, traffic systems, industrial and retail applications, and monitoring wildlife habitats. She has established, and is directing the Smart Vision Systems Laboratory at Syracuse University.
Yingbin Liang’s research focuses on developing physical layer innovations for enhancing reliability and security for wireless communications, analyzing the impact of these technologies on the fundamental performance limits of wireless networks, and applying novel machine learning techniques for designing efficient decision making algorithms for wireless networks.
Pramod Varshney (IEEE Fellow), an internationally renowned leader in the field of Information Fusion, works in collaboration with several industry and military partners; his work includes the development and application of fusion and decision-making methodologies in networked systems.
Electrical Engineering & Computer Science